trriibbhhu uv vaann tunniiveerrssiityy …...figure 9.2 simulation of serial communication circuit...
TRANSCRIPT
TTRRIIBBHHUUVVAANN UUNNIIVVEERRSSIITTYY
IINNSSTTIITTUUTTEE OOFF EENNGGIINNEEEERRIINNGG
PPUULLCCHHOOWWKK CCAAMMPPUUSS
A
FINAL YEAR PROJECT REPORT
ON
AUTOMATIC TRAFFIC CONTROL SYSTEM USING DIGITAL IMAGE
PROCESSING
[EG777EX]
By:
Bikalpa Khatiwada (Roll No. 065/BEX/409)
Hem Kanta Regmi (Roll No. 065/BEX/415)
Janak Kumar Lal (Roll No. 065/BEX/416)
Narayan Thapa (Roll No. 065/BEX/420)
A PROJECT SUBMITTED TO THE DEPARTMENT OF ELECTRONICS AND
COMPUTER ENGINEERING IN PARTIAL FULFILLMENT OF THE
REQUIREMENT FOR THE BACHELOR‟S DEGREE IN
ELECTRONICS AND COMMUNICATION ENGINEERING
DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING
LALITPUR, NEPAL
October, 2012
ii
COPYRIGHT
The author has agreed that the Library, Department of Electronics and Computer
Engineering, Pulchowk Campus, Institute of Engineering may make this report freely
available for inspection. Moreover, the author has agreed that permission for extensive
copying of this project report for scholarly purpose may be granted by the supervisors who
supervised the project work recorded herein or, in their absence, by the Head of the
Department wherein the project report was done. It is understood that the recognition will
be given to the author of this report and to the Department of Electronics and Computer
Engineering, Pulchowk Campus, Institute of Engineering in any use of the material of this
project report. Copying or publication or the other use of this report for financial gain
without approval of to the Department of Electronics and Computer Engineering,
Pulchowk Campus, Institute of Engineering and author‟s written permission is prohibited.
Request for permission to copy or to make any other use of the material in this report in
whole or in part should be addressed to:
Head of Department
Department of Electronics and Computer Engineering
Pulchowk Campus, Institute of Engineering
Pulchowk, Lalitpur
Nepal
iii
ACKNOWLEDGEMENT
First of all, we would like to express our deep sense of gratitude to our project supervisor,
Dr. Surendra Shrestha, Associate Professor, Department of Electronics and
Computer Engineering, Pulchowk Campus, for providing a lot of inspiration and
intellectual guidance while being very encouraging and supportive.
We are highly indebted to the Department of Electronics and Computer Engineering,
Pulchowk Campus for providing this opportunity of collaborative undertaking which has
helped us develop a major project of our own that greatly enhances our knowledge and
provides a new experience of team-work, quite important for future career.
We would like to express our sincere gratitude to lecturer Dr. Sanjeeb Prasad Panday for
giving us enough time to provide us necessary guidance on image processing despite his
busy schedule.
We would like to express our sincere gratitude to the Robotics Club, Pulchowk Campus
and its members for providing us their invaluable assistance throughout the development
of the hardware part of this project.
We would like to thank all the teachers, friends and seniors for providing valuable
suggestions regarding the project.
Bikalpa Khatiwada ([email protected])
Hem Kanta Regmi ([email protected])
Janak Kumar Lal ([email protected])
Narayan Thapa ([email protected])
iv
ABSTRACT
Traffic lights at most road intersections operate on a fixed timing schedule that leads to
suboptimal traffic management, with unnecessary delays and thus lead to higher fuel
consumption and higher emissions. In this project, a system to detect vehicles and thus
determine their density in each street (or lane), based on digital image-processing
techniques has been developed. The project is, technically, divided into two parts – the
software part and the hardware part. The software part deals with processing of the images
of the vehicle traffic to generate the control decisions. The images used for processing can
be taken by pre-installed traffic surveillance cameras that are fixed at the top of the
existing traffic lights or fixed somewhere close to them. The hardware part deals with
controlling the traffic lights using an interface module on the basis of control decisions
provided by the software part. Although the system developed in this project cannot be
taken as an alternative, it can still be an option to facilitate the existing manual traffic
management system of Nepal.
Keywords: Traffic management, Traffic lights, Digital image processing, Vehicle density
detection
v
TABLE OF CONTENTS
COPYRIGHT......................................................................................................................... ii
ACKNOWLEDGEMENT .................................................................................................... iii
ABSTRACT.......................................................................................................................... iv
TABLE OF CONTENTS....................................................................................................... v
LIST OF FIGURES .............................................................................................................. ix
LIST OF TABLES ................................................................................................................. x
LIST OF ACRONYMS ........................................................................................................ xi
1. INTRODUCTION .......................................................................................................... 1
1.1. Background ............................................................................................................. 1
1.2. Objectives ................................................................................................................ 2
1.3. Traffic System Overview ........................................................................................ 3
1.3.1. Traffic Management System ............................................................................ 3
1.3.2. Traffic Junction Structure ................................................................................ 4
1.3.3. Traffic Light System ........................................................................................ 6
2. LITERATURE REVIEW ............................................................................................... 8
3. BACKGROUND THEORY ........................................................................................... 9
3.1. Traffic Engineering ................................................................................................. 9
3.1.1. General Principles of Signal Design .............................................................. 11
3.1.2. General Design Data for Traffic Intersection System ................................... 12
3.1.3. Trial Method of Cycle Design ....................................................................... 12
3.2. Digital Image Processing ...................................................................................... 13
3.2.1. Image Acquisition .......................................................................................... 13
3.2.2. Image Enhancement ....................................................................................... 14
3.2.3. Morphological Processing ............................................................................. 17
3.2.4. Segmentation ................................................................................................. 18
vi
3.3. Embedded System ................................................................................................. 19
3.3.1. Microcontroller .............................................................................................. 21
4. SYSTEM DEVELOPMENT ........................................................................................ 22
4.1. Preliminary investigation phase ............................................................................ 22
4.1.1. Problem in Existing System ........................................................................... 22
4.1.2. Application and Scope of Project .................................................................. 22
4.2. Problem Analysis Phase ........................................................................................ 23
4.2.1. Problem Domain ............................................................................................ 23
5. METHODOLOGY ....................................................................................................... 24
5.1. Traffic Detection ................................................................................................... 24
5.1.1. Manual Detection ........................................................................................... 24
5.1.2. Automatic Detection ...................................................................................... 24
5.2. Functional Block Diagram of the System ............................................................. 25
5.3. Image Processing Algorithms ............................................................................... 27
5.3.1. Image Acquisition .......................................................................................... 27
5.3.2. Image Enhancement ....................................................................................... 28
5.3.3. Background Subtraction ................................................................................ 29
5.3.4. Processing Upper Half of the Image .............................................................. 32
5.3.5. Processing Lower Half of the Image ............................................................. 34
6. HARDWARE IMPLEMENTATION .......................................................................... 36
6.1. Control Interface ................................................................................................... 36
6.1.1. Specification of Control Interface.................................................................. 36
6.2. Communication with PC ....................................................................................... 37
6.3. Current Amplifier .................................................................................................. 38
6.4. Traffic Lights Circuit ............................................................................................ 39
6.5. Tools Used for Simulation .................................................................................... 40
6.5.1. Proteus VSM .................................................................................................. 40
vii
6.5.2. Livewire ......................................................................................................... 41
6.6. Tools used for Circuit Design ............................................................................... 41
6.6.1. PCB Wizard ................................................................................................... 41
6.6.2. KiCAD ........................................................................................................... 41
6.7. Tools used for Microcontroller Programming ...................................................... 42
6.7.1. MPLAB .......................................................................................................... 42
7. SOFTWARE IMPLEMENTATION AND SIMULATION ........................................ 43
7.1. Programming Language and Tools ....................................................................... 43
7.2. Simulation Software/GUI...................................................................................... 43
8. PROBLEM FACED AND SOLUTIONS .................................................................... 46
8.1. Some Solved Problems ......................................................................................... 46
8.1.1. Software ......................................................................................................... 46
8.1.2. Hardware ........................................................................................................ 46
8.2. Some Unsolved Problems ..................................................................................... 47
8.2.1. Software ......................................................................................................... 47
9. RESULT AND ANALYSIS......................................................................................... 48
9.1. Experimental Setup ............................................................................................... 48
9.2. Hardware Testing .................................................................................................. 48
9.2.1. Basic Circuit Testing ..................................................................................... 48
9.2.2. Testing the Fixed Time System ..................................................................... 48
9.2.3. Testing the Traffic Light Circuit .................................................................... 49
9.2.4. Connecting the Control Interface and Traffic Light Circuit .......................... 49
9.2.5. Connecting Control Interface and PC ............................................................ 50
9.2.6. Connecting All Three Units and Testing ....................................................... 50
9.3. Software Testing ................................................................................................... 51
10. COST ANALYSIS OF THE PROJECT ................................................................... 53
11. LIMITATIONS AND FUTURE ENHANCEMENTS ............................................. 55
viii
11.1. Limitations ............................................................................................................ 55
11.2. Future Enhancements ............................................................................................ 55
12. CONCLUSION ......................................................................................................... 57
13. REFERENCES ......................................................................................................... 58
APPENDIX .......................................................................................................................... 60
Appendix A ...................................................................................................................... 60
Appendix B ...................................................................................................................... 61
Appendix C ...................................................................................................................... 62
Appendix D ...................................................................................................................... 64
Appendix E ...................................................................................................................... 65
Appendix F ....................................................................................................................... 67
Appendix F ....................................................................................................................... 68
Appendix G ...................................................................................................................... 69
ix
LIST OF FIGURES
Figure 1.1 Manual Traffic Management at Thapathali .......................................................... 4
Figure 1.2 Structure of Left-Handed Traffic Junction ........................................................... 5
Figure 1.3 Structure of Right-Handed Traffic Junction ......................................................... 5
Figure 1.4 Basic Structure of Traffic Light ........................................................................... 7
Figure 3.1 Block Diagram of DIP with Respect to this project ........................................... 13
Figure 5.1 Functional Block Diagram of Automatic Traffic Control System ..................... 25
Figure 5.2 DIP Algorithms Used in System Development .................................................. 28
Figure 5.3 Conventional Background Subtraction Procedure ............................................. 30
Figure 5.4 Original Image .................................................................................................... 31
Figure 5.5 Image With Only Objects of Interest ................................................................. 31
Figure 5.6 Upper Half Portion ............................................................................................ 32
Figure 5.7 Lower Half Portion ............................................................................................. 32
Figure 5.8 Thresholded Image ............................................................................................. 33
Figure 5.9 Clipping with Lines to get the Proper Count of Vehicle in the Image ............... 33
Figure 5.10 Dilated Image ................................................................................................... 34
Figure 5.11 Thresholded Image ........................................................................................... 34
Figure 6.1 Block Diagram of Hardware Implementation .................................................... 36
Figure 6.2 KiCAD Design of Control Interface .................................................................. 37
Figure 6.3 Circuit Diagram of a Darlington Pair using NPN Transistors............................ 38
Figure 6.4 LED Connections Using Darlington Pair ........................................................... 39
Figure 6.5 PCB Design of Traffic Light Circuit .................................................................. 40
Figure 7.1 Screenshot of the GUI ........................................................................................ 44
Figure 9.1 Simulation of Traffic Light Circuit in Proteus ................................................... 49
Figure 9.2 Simulation of Serial Communication Circuit in Proteus .................................... 50
Figure 9.3 A Frame Showing the Road Traffic ................................................................... 51
Figure 9.4 Section of GUI Showing the Number of Vehicles ............................................. 52
x
LIST OF TABLES
Table 9.1 Cost Analysis of the Project
xi
LIST OF ACRONYMS
2D Two Dimensional
3D Three Dimensional
BSD Berkeley Software Distribution
CAD Computer Aided Design
CC Closed Circuit
CCD Charge Coupled Device
CMOS Complementary Metal–Oxide–Semiconductor
CUDA Compute Unified Device Architecture
DIP Digital Image Processing
DU Decision Unit
GPU Graphical Processing Unit
GUI Graphical User Interface
HCI Human – Computer Interaction
IDE Integrated Development Environment
IPU Image Processing Unit
I/O Input/Output
LCD Liquid Crystal Display
LED Light Emitting Diode
MATLAB MATrix LABoratory
MPLAB MicroProcessor LABoratory
mA milli-Ampere
xii
OS Operating System
OTP One Time Programmable
PC Personal Computer
PCB Printed Circuit Board
PDA Personal Digital Assistant
RAM Random Access Memory
RFID Radio – Frequency Identification
ROM Read Only Memory
RTOS Real Time Operating System
SDK Software Development Kit
SFM Structure From Motion
SPICE Simulation Program for Integrated Circuits Emphasis
SQL Structured Query Language
SVN SubVersioN
TTL Transistor – Transistor Logic
USB Universal Serial Bus
VIP Very Important Person
VSM Virtual System Modelling
1
1. INTRODUCTION
1.1. Background
The increase in urban traffic brings with it the problem of frequent traffic jams at major
traffic junctions. This requires for an efficient traffic management system to be
implemented at such places. Commonly, special organized government body such as
Metropolitan Traffic Police Division (as in case of Nepal) is established to manage the
vehicle traffic of the streets. The traffic personnel are responsible to look after the traffic
conditions and take necessary steps to eliminate any sort of traffic congestions. A special
arrangement of traffic lights is installed at every junction to control the vehicle traffic of
every constituent street.
In metropolitan cities or urban areas of the developed nations where the number of vehicles
is proportional to the width of the streets, these traffic lights are so adjusted that they turn
on and off at certain intervals of time and allow the vehicles to move across the junction
after every certain time interval. Similar traffic light systems can also be found installed in
the traffic junctions of major cities of Nepal such as Kathmandu. Unlike the streets of
foreign countries, the width of the streets is not sufficient enough to serve the vehicle
traffic that presently exists in the city. Besides, the streets of Kathmandu remain
overcrowded by the vehicles especially during the day time on weekdays. Hence, the
traffic light system that switches the traffic lights at certain fixed intervals of time
regardless of the vehicle traffic on different streets is quite ineffective to manage the traffic
at the junction. Moreover, due to the unnecessary regular power cuts as well, the available
traffic lights also cannot be operated as and when needed and hence cannot be relied upon.
As a result, during such time periods of the daytime, a group of 6 to 10 traffic personnel
with 4 - 4 hours shift in-between is mobilized at each traffic junction and they are assigned
the responsibility to eliminate the unnecessary problem of traffic jams.
Furthermore, there is no special arrangement of traffic lights to enforce traffic rules and
regulations outside Kathmandu valley. Hence, the traffic personnel need to be fully
dedicated and responsible to enforce traffic rules and regulations so as to manage vehicle
traffic in all those places.
2
Depending upon the vehicle traffic, the traffic personnel allow the vehicles from different
streets to move across junction (to another street) for different time intervals. Thus, they
attempt to avoid any instantaneous traffic jam(s) at the junction.
The manual steps taken in favour of traffic management as explained above can also be
automated. Especially in a country like Nepal, where the vehicle traffic equally contributes
to air pollution, there is a great need of automated traffic control system so that the traffic
personnel can avoid themselves from the adverse consequences of air pollution.
Traffic signals generated solely by timer based circuit that operate according to the
predetermined timing sequence may not always be appropriate to manage the vehicle
traffic. Hence, when not manually operated, the traffic signals should be generated by real-
time processing of the vehicle traffic so that it can compensate for the varying traffic
density and better manage the traffic.
Automatic traffic management system can be achieved through one or the integration of
the following systems: wireless communications, computational technologies, floating
cellular data, sensing technologies, inductive loop detection, and video vehicle detection.
In this project, the automation of the traffic control system has been implemented on the
basis of the digital image processing. [1]
Digital image processing is associated with the various operations that are performed to
process the digital images for various purposes such as image enhancement, image
restoration and likewise. In context of this project, it has been extensively employed to
process the real-time images of the vehicles that constitute the road traffic and on the basis
of the density of the vehicles in the road, the appropriate traffic signals are generated.
1.2. Objectives
The main objective of this project is to build an automatic system that takes decision in
real time to control traffic lights using digital image processing. The system measures the
traffic queue length using image processing sensors and generates necessary control
signals. For this purpose, an integrated system of hardware for controlling the traffic lights
and software for image processing has been designed that accomplishes the task of
3
automatic traffic management and overcomes the short comings of the existing traffic
management system. Apart from this, the software developed in this project also simulates
the traffic light control system and facilitates the surveillance of road traffic as well as
helps to monitor the functioning of the traffic lights from a single computer or laptop in
which the software has been installed.
1.3. Traffic System Overview
1.3.1. Traffic Management System
In general, the traffic management system comprises the traffic lights fixed permanently at
appropriate places at the traffic junction and a control system designed to operate them,
which may be kept in the vicinity of the traffic lights or somewhere remotely located. The
vehicles should move (or stop) in accordance with the traffic signal produced by the traffic
light(s). Although traffic lights have been installed at various traffic junctions throughout
the urban areas of Kathmandu valley, they do not yield optimum outputs for effective
traffic management for various reasons. As a result, the task of traffic management is
assigned to the traffic personnel mobilized by the Metropolitan Traffic Police Division.
Hence, in one way or another, the traffic management system in Nepal is manual as shown
in Figure 1.1.
Apart from this, the traffic system of a country is either right handed or left-handed.
Despite some exceptional cases such as the segment of road that lies between Rani Pokhari
and Ratna Park which is right-handed, Nepal has left-handed traffic system i.e. the traffic
moves forward along the left side of the road.
In Nepal, no separate lanes have been designed for the four wheelers and two wheelers.
Both four and two wheelers share the same lane such that no distinct rules to overtake
vehicles have been specified by the Metropolitan Traffic Police Division as well.
The pedestrians are allowed to cross the road using the zebra-crossing or the sky bridge.
Unlike the sky bridge, which is accessible to the pedestrians at all times as it is built above
the roads, the zebra-crossings are often placed at the traffic junctions and hence, can be
used only when the vehicles along the lane which has the zebra-crossing are stopped as per
the traffic signals.
4
Figure 1.1 Manual Traffic Management at Thapathali
1.3.2. Traffic Junction Structure
A general structure of the traffic junction is shown in Figures 1.2 and 1.3. The Figures 1.2
and 1.3 correspond to the left-handed and right-handed traffic systems respectively. In the
left-handed traffic system, the vehicles are always allowed to make left turning.
5
Figure 1.2 Structure of Left-Handed Traffic Junction
The arrows show the directions along which the vehicles from each street are allowed to
move across the junction i.e. the vehicles are free to move from West to North, East to
South, South to West and North to East.
Normally, the movement of vehicles or traffic across the junction is done in the following
order [1]:
STEP 1: The traffic is allowed to move in W – S and W – E direction.
STEP 2: The traffic is allowed to move in N – S and N – W direction.
STEP 3: The traffic is allowed to move in E – W and E – S direction.
STEP 4: The traffic is allowed to move in S – N and S – E direction.
Figure 1.3 Structure of Right-Handed Traffic Junction
6
All the movements of vehicles mentioned above are given equal time in case of normal
traffic conditions. Here the normal traffic means the one in which traffic density is
relatively equal in all the streets.
If the density in one direction is relatively more in comparison to others then the time for
the movement of the vehicles along that direction is increased and if the density of traffic is
relatively less than others and is also less than that during normal condition then the time
for movement of vehicles along that direction is decreased. In such cases, the traffic
management is not bound by the rule to follow above steps.
1.3.3. Traffic Light System
Traffic signals consist of an arrangement of different coloured lights which are collectively
called traffic lights. The traffic lights are signaling devices positioned at road intersections,
pedestrian crossings and other locations to control competing flows of traffic. The simple
and/or basic arrangement of traffic lights has been indicated by the alphabets A, B, C and
D in Figures 1.2 and 1.3. Depending upon the intensity of vehicles arriving at the junction,
the number and positions of the traffic light arrangement may vary.
The traffic light system usually consists of three types of traffic lights each representing
three different colours. They are as follows:
I) Red
II) Amber
III) Green
I) Red
The red light is used to indicate the vehicles to stop at traffic junction. The main reason to
choose red light for this purpose was that the colour red generally symbolizes danger or
warning. Apart from this, the red light can be distinguished very correctly from a great
distance and even under poor visibility conditions such as during fog.
7
II) Amber
The amber or yellow light is used to indicate that the red light is about to glow next at any
instant. The yellow colour is generally used as a warning or caution symbol. Besides, it
contrasts well with the colours green and red, and is highly visible without being too harsh
on the eyes of vehicle riders.
III) Green
The green light is used to indicate the vehicles to move across the junction if they were
previously stopped.
In short green light means „go‟ and red light means „stop‟. The basic structure of a traffic
light is shown in Figure 1.4.
- R represents Red light.
- Y represents Yellow or Amber light.
- G represents Green light.
Figure 1.4 Basic Structure of Traffic Light
R
Y
G
8
2. LITERATURE REVIEW
The projects addressing similar problems have been done at both national and international
levels. Those projects also perform the automatic control of traffic on the basis of digital
image processing. The projects that were done at national level include “Model Based
Automatic Traffic Control System” by Indu Bhandari and Kamal K.C. and “Traffic
Control and Surveillance System” by Niraja Shakya, Sujana Gurung and Vandana Dhakal.
The project entitled “Model Based Automatic Traffic Control System” employed the DIP
algorithms such as background subtraction and basic global thresholding to accomplish the
task of automatic vehicle detection. This system produced satisfactory results on the
prototype of the traffic intersection that used miniature toy vehicles on the prototype to
simulate the traffic and the traffic density was determined after analyzing the video taken
by the web-camera.
The project entitled “Traffic Control and Surveillance System” implemented the DIP
algorithms such as edge detection and background subtraction to detect vehicles. This
system also consists of a buzzer system which is blown to indicate the pedestrians that it is
safe to cross the road. In both the projects mentioned above, MATLAB was used as the
tool for DIP.
In international level, many research works have been carried out for the automatic
detection of vehicles using DIP algorithms. Some of such works include “Image
Processing Based Intelligent Traffic Controller” done by Vikramaditya Dangi, Amol
Parab, Kshitij Pawar and S.S Rathod, and “Image Processing Algorithms for Detecting and
Counting Vehicles Waiting at a Traffic Light” done by Ernesto de la Rocha, Rafael
Palacios. Basically, the research work of Ernesto de la Rocha, Rafael Palacios has provided
a great help and guidance to develop the concept about the necessary image processing
algorithms that could be implemented for the automatic detection of vehicles using DIP
approach.
9
3. BACKGROUND THEORY
3.1. Traffic Engineering
At large intersections and crossing, right-turning traffic has to come across several
collision points, and hence, there is possibility of accident. At small intersection, vehicle
traffic is controlled by traffic personnel alternately showing stop signs to the incoming
traffic on roads. Traffic signals are such control device which can alternately direct the
traffic to stop and proceed at level intersections. The main requirement of traffic signals is
to draw attention, provide meaningful time to respond and have minimum waste of time.
[2]
There are many advantages and disadvantages of traffic signals. Some of the advantages
can be listed as follows:
- Movement of traffic on level intersection is more orderly and safe.
- Traffic handling capacity of the intersection is increased.
- Right angled collision points are reduced thus reducing the accident possibilities.
- Traffic signals when subjected to automatic control prove to be more economical
than manually operated signals.
- Due to stoppage at intersections, compact streams of traffic are formed which
improve flow of traffic.
- If signal system at several intersections is properly co-ordinated, reasonably good
speed of traffic on the main road can be maintained.
- Pedestrians can cross the roads safely at the signalised intersection.
- The signals allow crossing of the heavy traffic flow with safety.
- Signals provide chance to crossing traffic of minor road to cross the path of
continuous flow stream at reasonable intervals of time.
Similarly, the disadvantages of having traffic signals can be listed as follows:
- In case of automatic control, failure of power may cause lot of confusion.
- Possibilities of rear-end collision are increased.
- Incorrectly designed and located signals may induce traffic violations.
10
- In case of automatic signals, operated at fixed interval, a vehicle in urgency may
have to wait at intersection even if there is no traffic on crossing road, only because
signal is showing red light to him.
The capacity of a traffic intersection, operated by signals is dependent on roadway width,
number of lanes, geometric standards, operational and control factors.
With the advancement in the field of transportation and increase in the use of vehicles, the
need for advanced and improved traffic management system became vital. This led to the
invention of various types of traffic control signals. The types of traffic control signals are
as follows:
I) Manually operated light signals.
II) Fixed-time automatic light signals.
III) Traffic actuated light signals.
I) Manually Operated Light Signals
Light signal posts are fixed at the left hand side of the approaching vehicles at all the
entrance points to the intersection. These posts are provided with red, yellow amber and
green lights arrangements. These lights are so interconnected that they can be switched off
or switched on from a single common point.
II) Fixed-time Automatic Signals
These signals are also known as predetermined timing sequence traffic signals. They are
set to repeat at regular intervals at a specified sequence of signal indications. The timing of
each phase of cycle is determined based on traffic studies. They are operated automatically
with the help of electronic control. They have advantage of having less first cost, and with
a series of interconnected signals, vehicle can move through a series of intersections with
minimum of stops and delays. They may prove dangerous at occasions when due to some
defect vehicle may stop at the intersections. Similarly, one more drawback is that traffic on
the roads may have to wait because of red light and there may not be any traffic crossing
the road.
11
III) Traffic Actuated Signals
In this type of signals, timings of the phase and cycle can be changed according to the
traffic needs. In fully traffic-actuated signals detectors are located on each approach which
assigns the right way to various traffic movements on the basis of demand. On demand
from the cross street, the signal indication will changes at once if the main road had no
traffic.
3.1.1. General Principles of Signal Design
Certain standards or rules need to be followed so as to design the traffic signalling
mechanism or apparatus. This is mainly done so as to bring uniformity in the similar
approach implemented throughout a region or a country. Some of such principles of signal
design are as follows:
1) Red phase (explained in Appendix A) R1 i.e. the time duration for which the red
light is illuminated, of a signal is the sum of green and amber phases.
2) Near the end of the red phases, amber lights are switched on along with the red
lights to indicate „get set and go‟. This phase is known as „red amber‟ or „initial
amber‟ phase. The vehicles are not supposed to cross the stop line, which may
usually be the zebra crossing, during red amber phase.
3) Clearance amber or clearance time is provided just after green phase before the red
phase to fulfil following two requirements:
i. Stopping time for the approaching vehicle to stop at the stop line after the
signal changes from green to amber and not to cross the line by the time the
signal changes to red phase.
ii. Clearance time for the vehicle which is approaching the stop line at
permissible speed while the signal changes from green to amber. Usually
interval of 2 to 4 seconds is fixed for the amber phase.
4) Green time is decided based on the approach volume or the quantity/number of
vehicles arriving at the traffic junction during peak hour and the sufficient time
required by the queued vehicles to clear off in most of the cycles.
12
3.1.2. General Design Data for Traffic Intersection System
1. Cycle length (explained in Appendix A) for two phase signal is mostly 40 to 60
seconds.
2. Timing of yellow or amber colour varies from 3 to 5 seconds.
3. Timing for green light may be nearly 20 seconds.
4. Timing for red light is slightly less than green light.
5. Clearance of pedestrian time is calculated on the basis of average walking speed
of the pedestrians, which is usually defined to be 1.2 metres per second.
3.1.3. Trial Method of Cycle Design
Let A and B be two roads intersecting at a point and it is required to design cycle length for
this intersection.
i. For 15 minutes take traffic counts on road A and B, simultaneously at the same
intersection.
ii. Let N1 and N2 be the traffic counts of 15 minutes for A and B roads respectively.
iii. Assume suitable trial cycle length of C seconds.
iv. Based on the assumed value of C, calculate the number of cycles in 15 minute
period as follows [2]:
(2.1)
v. Assume 2.5 seconds head-way time and calculate green light periods GA and GB for
roads A and B respectively as follows [2]:
(2.2)
(2.3)
vi. Assume yellow or amber periods of YA and YB for A and B roads.
13
vii. Calculate the time [2]:
(2.4)
viii. If the calculated cycle length C1 works out approximately equal to the assumed
cycle, C, the cycle length is accepted as design cycle. Otherwise trials are repeated.
3.2. Digital Image Processing
Digital image processing encompasses processes whose inputs and outputs are images and,
in addition, encompasses processes that extract attributes from images, up to and including
recognition of the individual objects. [3]
An image may be defined as two-dimensional function f(x, y), where x and y are spatial
(plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the
intensity or gray level of the image at that point. When x, y, and the amplitude values of f
are all finite, discrete quantities, we call the image a digital image. The field of digital
image processing refers to the processing of the digital images by means of digital
computer. A digital image is composed of a finite number of elements, each of which has a
particular location and value. These elements are referred to as picture elements or pixels.
[3]
Figure 3.1 Block Diagram of DIP with Respect to this project
3.2.1. Image Acquisition
Images are generated by the combination of an “illumination” source and the reflection or
absorption of energy from that source by the elements of the “scene” being imaged.
Image Acquisition
Image Enhancement
Morphological Processing
Segmentation
14
An image sensor is a device that converts an optical image into an electronic signal. It is
used mostly in digital cameras, camera modules and other imaging devices. Early analog
sensors were video camera tubes, most currently used are digital charge-coupled device
(CCD) or complementary metal–oxide–semiconductor (CMOS) active pixel sensors.
Most digital still cameras use either a CCD image sensor or a CMOS sensor. Both types of
sensor accomplish the same task of capturing light and converting it into electrical signals.
A CCD image sensor is an analog device. When light strikes the chip it is held as a small
electrical charge in each photo sensor. The charges are converted to voltage one pixel at a
time as they are read from the chip. Additional circuitry in the camera converts the voltage
into digital information.
A CMOS imaging chip is a type of active pixel sensor made using the CMOS
semiconductor process. Extra circuitry next to each photo sensor converts the light energy
to a voltage. Additional circuitry on the chip may be included to convert the voltage to
digital data.
A digital camera (or digicam) is a camera that takes video or still photographs by recording
images on an electronic image sensor. Most cameras sold today are digital and digital
cameras are incorporated into many devices ranging from PDAs and mobile phones (called
camera phones) to vehicles.
Digital and film cameras share an optical system, typically using a lens with a variable
diaphragm to focus light onto an image pickup device. The diaphragm and shutter admit
the correct amount of light to the imager, just as with film but the image pickup device is
electronic rather than chemical. However, unlike film cameras, digital cameras can display
images on a screen immediately after being recorded, and store and delete images from
memory. Many digital cameras can also record moving video with sound. Some digital
cameras can crop and stitch pictures as well as perform other elementary image editing.
3.2.2. Image Enhancement
Image enhancement comprises the algorithms that make necessary changes to the original
images so that they can be made more useful for further processing. It eliminates the noise
15
introduced in the images either during acquisition or during digitization. Apart from this, it
is used to make the lighting adjustments in the images that are taken under unfavourable
lighting conditions such as during gloomy day or in a poorly illuminated room. With
regards to this project, image enhancement has been implemented to eliminate the noise
using the median and low-pass Gaussian filter and adjust the shortcomings of lighting
conditions using the histogram equalization technique.
I) Median Filter
In median filtering, the neighbouring pixels are ranked according to brightness (intensity)
and the median value becomes the new value for the central pixel. Median filters can do an
excellent job of rejecting certain types of noise, in particular, “shot” or impulse noise also
called “salt and pepper” in which some individual pixels have extreme values. In the
median filtering operation, the pixel values in the neighbourhood window are ranked
according to intensity, and the middle value (the median) becomes the output value for the
pixel under evaluation.
There is no reduction in contrast across steps, since output values available consist only of
those present in the neighbourhood (no averages). Median filtering does not shift
boundaries, as can happen with conventional smoothing filters (a contrast dependent
problem). Since the median is less sensitive than the mean to extreme values (outliers),
those extreme values are more effectively removed. The median is, in a sense, a more
robust “average” than the mean, as it is not affected by outliers (extreme values). Since the
output pixel value is one of the neighbouring values, new “unrealistic” values are not
created near edges. Since edges are minimally degraded, median filters can be applied
repeatedly, if necessary.
II) Gaussian Filter
Edges and other sharp transitions (such as noise) in the gray levels of an image contribute
significantly to the high-frequency component of its Fourier transform. Hence, smoothing
is achieved in the frequency domain by attenuating a specified range of high-frequency
components in the transform of a given range.
16
Basic model of the filtering in the frequency domain is given by the following equation
[3]:
G(u, v) = H(u, v)F(u, v) (2.5)
F(u, v) is the Fourier transform of the image to be smoothed.
H(u, v) is the filter transfer function.
G(u, v) is output of the process.
Gaussian low-pass filter is one of the filters used to eliminate the edges and other sharp
transitions (such as noise) in the gray levels from an image.
The form of the Gaussian low-pass filtering in the 2-D is given by [3]:
( ) ( ) (2.6)
D(u, v) is the distance from the origin of the Fourier transform, which is assumed to be
shifted to the centre of the frequency rectangle.
III) Histogram Equalization
Histogram equalization is a method in image processing of contrast adjustment using the
image's histogram. This method usually increases the global contrast of many images,
especially when the usable data of the image is represented by close contrast values.
Through this adjustment, the intensities can be better distributed on the histogram. This
allows for areas of lower local contrast to gain a higher contrast.
Histogram equalization accomplishes this by effectively spreading out the most frequent
intensity values. The method is useful in images with backgrounds and foregrounds that
are both bright or both dark.
Courtesy: “Histogram equalization” http://en.wikipedia.org/wiki/Histogram_equalization, 2012
17
3.2.3. Morphological Processing
The basic morphological transformations are called dilation and erosion, and they arise in a
wide variety of contexts such as removing noise, isolating individual elements, and joining
disparate elements in an image. Morphology can also be used to find intensity bumps or
holes in an image and to find image gradients. [4]
I) Dilation
Dilation is a morphological transformation of the image that causes bright regions within
an image to grow. [4]
Dilation is a convolution of some image (or region of an image), say A, with some kernel,
say B. The kernel, which can be any shape or size, has a single defined anchor point. Most
often, the kernel is a small solid square or disk with the anchor point at the center. The
kernel can be thought of as a template or mask, and its effect for dilation is that of a local
maximum operator. As the kernel B is scanned over the image, the maximal pixel value
overlapped by B is computed and the image pixel under the anchor point is replaced with
that maximal value. This causes bright regions within an image to grow. This growth is the
origin of the term “dilation operator”. [4]
II) Erosion
Erosion is the converse of dilation. The action of the erosion operator is equivalent to
computing a local minimum over the area of the kernel. Erosion generates a new image
from the original using the following algorithm: as the kernel B, similar to the one
mentioned above, is scanned over the image, the minimal pixel value overlapped by B is
computed and the image pixel under the anchor point is replaced with that minimal value.
[4] This causes the narrow connected regions in the image to diminish.
In general, whereas dilation expands region A, erosion reduces region A. Moreover,
dilation will tend to smooth concavities and erosion will tend to smooth away protrusions.
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Of course, the exact result will depend on the kernel, but these statements are generally
true for the filled convex kernels typically used. [4]
3.2.4. Segmentation
Segmentation subdivides an image into its constituent regions or objects. The level to
which subdivision is carried depends on the problem being solved. That is, segmentation
should stop when the object of interest in an operation has been isolated. In this project, the
thresholding algorithms have been extensively used for the purpose of segmentation [3].
Thresholding
Consider an image, f(x, y), composed of light objects on a dark background, in such a way
that object and background pixels have gray levels grouped into two dominant modes. One
obvious way to extract the objects from the background is to select a threshold T that
separates these modes. Then any point (x, y) for which f(x, y)>T is called an object point;
otherwise, the point is called a background point. When T depends only on f(x, y) (that is,
only gray-level values) the threshold is called global. If T depends on both f(x, y) and
p(x,y), which is some local property of the point (x,y), the threshold is called local. If, in
addition, T depends on the spatial coordinates x and y, the threshold is called dynamic or
adaptive. [3]
I) Basic Global Thresholding
The simplest of all thresholding technique is to partition the image histogram by using a
single global threshold, T. Segmentation is then accomplished by scanning the image pixel
by pixel and labelling each pixel as object or background, depending upon whether the
gray level of that pixel is greater or less than the value of T. The success of this method
depends entirely on how well the histogram can be partitioned [3].
The following algorithm is used to obtain T:
1. Select an initial estimate of T.
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2. Segment the image using T. This will produce two groups of pixels: G1 consisting
of all pixels with gray level values >T and G2 consisting of pixels with values ≤ T.
Compute the average gray level values μ1 and μ2 for the pixels in the regions G1 and
G2.
3. Compute the new threshold value [3] :
(2.7)
4. Repeat steps 2 through 4 until the difference in T in successive iterations is smaller
than a predefined parameter T0.
II) Adaptive Thresholding
Adaptive thresholding is used in case when a histogram cannot be partitioned effectively
by a single global threshold. An approach for handling such a situation is to divide the
original image into sub-images and then utilize a different threshold to segment each sub-
image. The image is subdivided into smaller sub-images, such that the illumination of each
sub-image is approximately uniform. Since the threshold used for each pixel depends on
the location of the pixel in terms of the sub-images, this type of thresholding is called
adaptive thresholding [3].
3.3. Embedded System
An embedded system is a computer system designed to perform one or a few dedicated
function often with real-time computing constraints. It is embedded as part of a complete
device often including hardware and mechanical parts. An embedded system can be
defined as “A system whose principal function is not computational, but which is
controlled by a computer embedded within it. The computer is likely to be a
microprocessor or microcontroller. The word embedded implies that it lies inside the
overall system, hidden from view, forming an integral part of greater whole.”
Physically, embedded systems can be found in wide range of electronic equipments
ranging from portable devices such as digital watches and MP3 players, to large stationary
20
installations like traffic lights, factory controllers, or the systems controlling nuclear power
plants. Complexity varies from low, with a single microcontroller chip, to very high with
multiple units, peripherals and networks mounted inside a large chassis or enclosure.
Courtesy: “Embedded system” http://en.wikipedia.org/wiki/Embedded_system, 2012
An embedded system has three main components:
- Hardware
- Main application software
- Real Time Operating System (RTOS)
RTOS handles the execution, share resources among processes and schedules the tasks of
the system. A small scale embedded system may not need an RTOS. An embedded system
has software designed to keep in view three constraints; available system memory,
available processor speed and the need to limit power dissipation when running system
continuously.
Embedded system used in this project is small scale embedded systems. These systems are
designed with a single 8 or 16 bit microcontroller; they have little hardware and software
complexities and involve board level design. They may even be battery operated. When
developing embedded software, an editor, assembler and cross assembler, specific to the
microcontroller or processor used, are the main programming tools.
Usually, the C – language is used for developing these systems. Then, program
compilation is done into the assembly, executable codes are then appropriately located in
the system memory. The software has to fit within the memory available and keeping in
view the need to limit power dissipation when system is running continuously.
An embedded system designer has to develop a product using the available tools within the
given specifications, cost and time frame. Full understanding of microcontroller‟s basic
architecture, digital electronics design, software engineering, data communication, control
engineering, sensors and measurements, analog electronic design and PCB design and
manufacture are essential.
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3.3.1. Microcontroller
A microcontroller is a small computer mounted on a single integrated circuit containing a
processor core, memory and programmable input/output peripherals. Program memory in
the form of NOR flash or OTP ROM is also often included on chip, as well as a typically
small amount of RAM. Microcontrollers are designed for embedded applications, in
contrast to the microprocessors used in personal computers or other general purpose
applications.
Microcontrollers are used in automatically controlled products and devices, such as
automobile engine control systems, implantable medical devices, remote controls, office
machines, appliances, power tools, toys and other embedded systems. By reducing the size
and cost compared to a design that uses a separate microprocessor, memory, and
input/output devices, microcontrollers make it economical to digitally control even more
devices and processes. Mixed signal microcontrollers are common, integrating analog
components needed to control non-digital electronic systems.
A microcontroller can be considered a self-contained system with a processor, memory and
peripherals and can be used as an embedded system. The majority of microcontrollers in
use today are embedded in other machinery, such as automobiles, telephones, appliances,
and peripherals for computer systems. While some embedded systems are very
sophisticated, many have minimal requirements for memory and program length, with no
operating system, and low software complexity. Typical input and output devices include
switches, relays, solenoids, LEDs, small or custom LCD displays, radio frequency devices,
and sensors for data such as temperature, humidity, light level, etc. Embedded systems
usually have no keyboard, screen, disks, printers, or other recognizable I/O devices of a
personal computer, and may lack human interaction devices of any kind.
Courtesy: “Microcontroller” http://en.wikipedia.org/wiki/Microcontroller, 2012
22
4. SYSTEM DEVELOPMENT
4.1. Preliminary investigation phase
4.1.1. Problem in Existing System
Road and traffic engineering is very poor in Nepal. Even the traffic management of the
capital city, Kathmandu is not satisfactory despite the fact that a large number of traffic
personnel are mobilized for the same. Although traffic lights have been installed at various
traffic junctions throughout the city almost all of them are operated as per the
predetermined timing sequence. Hence, an effective traffic management system cannot be
expected.
Even the available traffic light systems are difficult to be maintained by any Nepalese
technician or engineer due to lack of either adequate knowledge or authority to do so. As a
result, once the traffic lights go out of order, they remain non-functional indefinitely.
So, whatever be the reason, the traffic management system has to be switched to manual
mode so as to achieve effective traffic management. In manual mode, the traffic personnel
immediately stand around the centre of the junction and direct the vehicles with hand
gestures to move or stop according to the traffic situation.
In context of Nepal especially in the urban areas, apart from being a very exhaustive job,
the traffic management also brings about the health related problems in the traffic
personnel as the vehicles contribute a lot to air pollution. As a result, there a great need of
automatic traffic control system for effective traffic management.
4.1.2. Application and Scope of Project
During the course of this project, the problems related to the existing traffic management
system of Nepal were identified. In this project, we look forward to solve these problems
by providing an automatic traffic control system that helps to achieve better traffic
management compared to contemporary timer circuit based traffic management. The
automatic system not only reduces the burden of traffic management from the traffic
personnel but also prevents them from being exposed to polluted environment. Apart from
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this, when traffic management is automated, the traffic personnel can get themselves
involved in other activities such as monitoring whether the vehicles abide by the traffic
rules, look for any particular vehicle that was stolen or was involved in some crime and
likewise.
Furthermore, the system developed in this project has been so designed that it can be easily
maintained by any person (or traffic personnel) who are capable enough to use a computer.
However, repairing or replacing the components of the hardware is not recommended
unless the person doing so has adequate knowledge of microcontroller and electronic
circuits.
4.2. Problem Analysis Phase
4.2.1. Problem Domain
The main purpose of this system is to automatically detect the vehicles present on the road.
Hence, this system needs to accomplish the following tasks to overcome the limitations of
the existing traffic management system:
i. Get sequence of frames from the real-time video of the road traffic.
ii. Detect the number or quantity of vehicles present in each frame using the image
processing algorithms.
iii. Based on the results of image processing, generate appropriate traffic signals that
control the traffic lights using an appropriate interface.
iv. Provide a GUI that helps to monitor the road traffic as well as control the
functioning of overall system.
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5. METHODOLOGY
5.1. Traffic Detection
Regardless of whether the traffic management system is manual or automatic, the first and
vital step in traffic management is to detect the vehicles present on the road. The extent
upto which vehicles can be detected correctly determines the level of success achieved in
traffic management. Detection through visual observation of roadways has been carried out
since for a long time. Recent development in technologies have shown that traffic detection
through real time video processing, loop detectors, inductive loop sensors and magnetic
sensors has been a noble and a promising approach to automatically detect traffic and its
parameters. [5]
5.1.1. Manual Detection
In manual detection, a traffic personnel or a group of them observe the traffic situation and
depending upon the situation, they take necessary actions to manage the traffic and
eliminate any sort of traffic jams. Although manual detection is efficient upto some extent
compared to the contemporary timer circuit based traffic lights system, it is still a very
tiring job apart from bringing about health related problems, due to air pollution, in the
traffic personnel.
5.1.2. Automatic Detection
Automatic detection of vehicles can be done using the real time video processing, loop
detectors, inductive loop sensors, magnetic sensors, infrared sensors or RFID based
systems. Inductive loop detectors do provide a cost-effective solution, however they are
subjected to a high failure rate when installed in poor road surfaces, decrease pavement life
and obstruct traffic during maintenance and repair. [6] Infrared sensors are affected to a
greater degree by fog than video cameras and cannot be used for effective surveillance. A
system based on Radio-Frequency Identification (RFID) requires that unnecessary extra
hardware to be installed both at every junction and in every vehicle. [6]
25
Among various methods of automatic vehicle detection such as ones mentioned above, the
method implemented in this project is based on digital image processing of the real-time
video of the road traffic. Video-based systems offer many advantages compared to other
automatic vehicle techniques. They provide more traffic information, combine both
surveillance and traffic control technologies, are easily installed, and are scalable with
progress in image processing techniques. [6]
In general, implementation of automatic traffic detection will eliminate the compulsion of
placing traffic personnel at various junctions to detect and manage the traffic.
5.2. Functional Block Diagram of the System
The automatic traffic control system designed in this system typically consists of one or
more cameras, a microprocessor based image processing unit (IPU) for digitizing and
processing the real-time video of the traffic, software for interpreting the video and
deriving appropriate traffic information from it, a microcontroller based decision unit (DU)
that controls the traffic lights and a computer simulation (software) of the control
operations. The entire system establishes a closed loop system with the IPU and the
decision unit forming the closed loop.
The functional block diagram of the system developed in this project is shown in Figure
5.1.
Figure 5.1 Functional Block Diagram of Automatic Traffic Control System
Camera (Input)
Image Processing
Unit
Decision Unit
Traffic Signal
Computer Simulation
26
I) Camera
The camera serves as the major input to the system. This camera can either be a normal
digital camera or special CC camera. During the course of this project, a normal digital
camera has been used to obtain the video footages of the road traffic. The video of the road
traffic acquired by the digital camera is transmitted to the IPU and is used for the analysis
of the traffic conditions.
II) Image Processing Unit
The image processing unit is a microprocessor based system and preferably a laptop or a
PC. This unit stores and runs specific software that obtains the video input of the road
traffic from the camera(s) and implements appropriate image processing algorithms on the
real-time video input(s) so as to extract necessary information about the vehicle traffic.
This software has been designed as an integral part of this project and facilitates to
accomplish the job of DIP. This software has been deliberately developed in such a way
that it does not store the video footages, as the processing is carried out on the real-time
video input(s).
III) Decision Unit
The decision unit follows the IPU and generates the appropriate signals to control the
traffic lights on the basis of the results provided by the IPU. The DU comprises a
microcontroller and ports for serial communication between the IPU and the traffic light
system. Thus, it also serves as an interface between the IPU and the traffic light system.
IV) Traffic Signal
The traffic signal is the major output of this system. The traffic signals are displayed using
the traffic lights.
27
V) Computer Simulation
Computer simulation is another part of the software installed in the IPU. This software is
integrated with the image processing software mentioned above. It shows the real time
images of all the streets of the traffic junction along with the condition of traffic lights for
the respective streets. Thus, it not only enables an operator (or traffic personnel) to monitor
the current traffic condition but also observe whether the traffic lights are being displayed
correctly. Hence, it provides sufficient controls to the operator to switch between automatic
and timing based traffic control. Furthermore, this software also enables the operator to
shutdown or suspend the traffic light system during special conditions such as upon the
arrival of ambulance in emergency from any street or upon the arrival of the vehicles of the
VIP.
5.3. Image Processing Algorithms
Image processing is the backbone of this project. It has been implemented to detect the
vehicles present on the road at any instant under consideration. In context of this project,
the image processing algorithms have been applied to the individual frames of a
continuous real-time video input, which is taken at regular intervals of time. In this system,
the image processing is implemented by the software installed in the IPU.
The image processing algorithms that have been implemented to accomplish the task of
automatic detection of vehicles in this system can be described in reference to the process
diagram shown in Figure 5.2.
5.3.1. Image Acquisition
As explained earlier, a digital camera or a special CC camera is used to obtain the real-time
video of the road traffic. During the development of this project, a normal digital camera
has been used to obtain the video footages of the road traffic. The images or the sequence
of the video frames thus obtained is first converted to grayscale images or video frames.
28
5.3.2. Image Enhancement
After the real-time video is obtained, the individual frames of the video, each of which
represent an image, are pre-processed in order to make them suitable for further
processing. In other words, only certain video frames that are taken after certain interval of
time are processed.
Figure 5.2 DIP Algorithms Used in System Development
Image
Acquisition
Image
Enhancement
Background
Subtraction
Dilation
Thresholding
Extract
Connected
Components
Count Blobs
Vehicle
Detection
Additional Noise
Removal
Thresholding
Count Pixels
29
The pre-processing mainly comprises the image enhancement technique. In case of this
system, histogram equalization followed by median and low-pass Gaussian filtering has
been employed as an image enhancement technique.
5.3.3. Background Subtraction
The main purpose of background subtraction is to remove any static or unnecessary objects
such as nearby buildings, electric poles, etc. from the video frame that do not contribute to
the vehicle detection procedure. Thus, the foreground or the objects of interest i.e. the
vehicles can be extracted from the background in a sequence of video frames.
There are many different methods that can be used for background subtraction, each with
different strengths and weaknesses in terms of performance and computational
requirements. The general procedure of background subtraction comprises preparation of a
background model. To do this, a sequence of video frames that do not contain any object
of interest or foreground (as far as possible) is considered. Then, the necessary information
from the individual frames such as intensity level of each pixel, the frame-by-frame
variation in the intensity level of each pixel, etc. is obtained. After that, all the information
is collectively used to approximate a background model that resembles the actual
background. Now, the sequences of video frames that contain the objects of interest are
considered. These frames are compared with the background model created earlier to
detect any section that does match the background model. This different section represents
the foreground or object of interest. The background subtraction procedure can be
illustrated by Figure 5.3.
Conventional methods of background subtraction require that the camera be free from any
sort of movements. This is needed so that the no any objects of the background are missed
during background modelling. As a result, such objects do not appear as foreground when
the sequences of video frames containing the objects of interest are processed.
Although the camera used to capture the road traffic is not subjected to significant panning,
it still encounters slight jerks when a heavy vehicle or four wheelers pass by the pole to
which it is fixed. As a result, the conventional method of background subtraction is quite
ineffective in case of the system developed in this project. Hence, it became necessary to
30
devise an entirely different approach. After thorough research and findings from hit and
trial, a different method of background subtraction has been employed to process the
images or video frames of the road traffic.
Figure 5.3 Conventional Background Subtraction Procedure
In the method of background subtraction used in this system, the first step is to clip the
image using mathematical modelling approach so that only the section of the frame that
comprises the road along with the vehicles is extracted. This results in the perspective view
of the road (containing vehicles). An example of this procedure is shown in Figures 5.4
and 5.5. Figure 5.4 shows an original frame and Figure 5.5 shows that only the region that
contains the object of interest has been selected for further processing.
The image thus obtained is divided into two different sections – upper and lower halves.
The main purpose of this division is to facilitate the task of vehicle detection using the
image processing algorithms. The region of the image represented by the upper half
contains vehicles in very compact pattern while that represented by the lower half contains
vehicles in quite distinct manner i.e. vehicles can be identified quite clearly than in upper
half. These characteristics can be observed in the Figure 5.4. Hence, two different methods
of DIP have to be implemented to process these two halves.
Video Input with No Object of Interest (as far as
possible)
Prepare Background Model
Video Input with Object of Interest
Compare Individual Frames with Background Model
Detect Foreground or Object of Interest
31
Figure 5.4 Original Image
Figure 5.5 Image With Only Objects of Interest
Considering Figure 5.5, the actual image i.e. image with only objects of interest is divided
into two halves along its centre. This process is shown in Figures 5.6 and Figure 5.7.
The two halves of the image thus obtained are processed in two different ways but
simultaneously.
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Figure 5.6 Upper Half Portion
Figure 5.7 Lower Half Portion
The processing of the two halves of the images as obtained in Figures 5.6 and 5.7 is done
in the following steps.
5.3.4. Processing Upper Half of the Image
I) Additional Noise Removal
The additional noise removal algorithm is mainly implemented to remove bright spots
from the image. These spots lead to the false calculation of vehicle density. So these kinds
of high frequency noises are removed by applying the low-pass Gaussian (smoothing)
filter. These noises mostly occur due to poor quality of camera or even due to the
unfavourable lighting conditions.
33
II) Thresholding
The image obtained after the Gaussian filtering is subjected to the segmentation process
based on thresholding method. In this method, the threshold value is taken to be little bit
higher (around 40 and obtained through experimentation) than the mean value of the
image.
Thus, the image obtained after threshold is a binary image which has only completely
white and black regions and no any intermediate values.
Figure 5.8 Thresholded Image
III) Count Number of Pixels
Finally, the pixels that have non-zero intensity values present in the resulting image are
counted. The counting is such that in the upper region, less number of pixels corresponds
to one vehicle and on moving towards the lower region of the image, relatively more
number of pixels corresponds to one vehicle.
As a mathematical modelling approach has been adopted, the vehicles that are near to the
camera appear to be illuminated relatively more than those that are far from the camera.
Hence, they dominate the vehicles farther from the camera in the image and the vehicle
counting is adversely affected.
Figure 5.9 Clipping with Lines to get the Proper Count of Vehicle in the Image
34
5.3.5. Processing Lower Half of the Image
I) Dilation
Dilation operation has been used in order to highlight any vehicle, especially ones with
brighter colour that are present on the lower half of the image being processed.
The result of dilation operation on the image shown in Figure 5.7 is illustrated in Figure
5.10.
Figure 5.10 Dilated Image
II) Thresholding
Thresholding has been applied to separate only the brighter objects from the image. The
darker objects are treated as the background and suppressed so that they are not considered
for further processing.
Figure 5.11 Thresholded Image
III) Extract Connected Components
After the thresholding operation has been carried out, the connected components from the
resulting image are extracted assuming that these components represent vehicles. The
connected components are extracted in the form of white blobs.
35
IV) Count Number of Blobs
The number of blobs present in the image of interest is counted using an iterative algorithm.
Finally the number of blobs gives the number of vehicles present in the region of interest
determined by the lower half of the original frame.
The results regarding the number or quantity of vehicles from both halves are combined so
as to finally obtain the number or quantity of vehicles present on the road. This vehicle
count is supplied to the decision unit and simulation software unit to control the timing of
the traffic lights for each road.
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6. HARDWARE IMPLEMENTATION
Figure 6.1 Block Diagram of Hardware Implementation
6.1. Control Interface
The control interface is the main interface circuit between the microcontroller, traffic lights,
and PC. The control interface used during this project is custom designed and self-fabricated
PIC16F877A (pin diagram shown in Appendix B) microcontroller based system. The inputs to
the control interface are the control signals from PC. Similarly, the outputs from the control
interface are the control signals to the traffic lights and data signals to PC. The operating
frequency of the crystal oscillator for PIC16F877A microcontroller is 11.0592MHz.
6.1.1. Specification of Control Interface
The control interface designed in this project is basically a microcontroller based system.
The specifications of this control interface are as follows:
i. Microchip PIC16F877A microcontroller used.
ii. Serial interface using MAX232 (pin diagram shown in Appendix C) for
communication between PC and the microcontroller.
iii. Interfacing of traffic lights unit.
iv. There is no effect on the operation of the control interface regardless of the polarity
of power at the input.
v. The control circuit consists of ULN 2803A (pin diagram shown in Appendix D) ICs
for the purpose of the current amplification.
vi. The control interface consists of bar graphs in order to monitor the status of the
pins.
IPU Control Interface Traffic Lights
37
Figure 6.2 KiCAD Design of Control Interface
6.2. Communication with PC
RS232 serial data communication protocol has been used to establish communication
between the PC and the microcontroller board. The method is also applied for
programming PIC16F877A through boot-loader by accessing its TX and RX pins.
MAX232 IC works as an interface between the TTL and RS232 standards as shown in
Figure 6.2. A USB to RS232 cable has been used to connect the USB port of PC to the
DB9 port of control interface board.
38
6.3. Current Amplifier
The current provided by the microcontroller alone is not enough to light the LED used as
the traffic lights. So, in order to fulfil the current requirement of the LED the current
amplification is needed, which has been carried out by the help of the Darlington transistor.
The Darlington transistor (often called a Darlington pair) is a compound structure
consisting of two bipolar transistors (either integrated or separated devices) connected in
such a way that the current amplified by the first transistor is amplified further by the
second one. This configuration gives a much higher common-emitter current gain than
each transistor taken separately and, in the case of integrated devices, can take less space
than two individual transistors because they can use a shared collector. Integrated
Darlington pairs are available in transistor-like packages or as an array of devices (usually
eight) in an integrated circuit. [8] A Darlington pair behaves like a single transistor with a
high current gain (approximately the product of the gains of the two transistors).
Darlington pair of transistor is also called as super-beta transistor. It has the capacity to
amplify the current output many a times than a normal transistor configuration.
A general relation between the compound current gain and the individual gains is given by:
[8]
(5.1)
If β1 and β2 are high enough (in order of hundreds mA), this relation can be approximated
with Equation 5.2.
(5.2)
Figure 6.3 Circuit Diagram of a Darlington Pair using NPN Transistors
39
The microcontroller triggers the base of the Darlington amplifier. The LED is connected
between the source and the collector of the Darlington. The amplified current flows
through the LED and it glows.
Figure 6.4 LED Connections Using Darlington Pair
Darlington pairs are available as integrated packages. The integrated package of Darlington
pairs used in the control interface of the system developed in this project is ULN 2083A.
The ULN2803A is a high-voltage, high-current Darlington transistor array. The device
consists of eight NPN Darlington pairs that feature high-voltage outputs with common-
cathode clamp diodes for switching inductive loads. The collector-current rating of each
Darlington pair is 500 mA. The Darlington pairs may be connected in parallel for higher
current capability.
Courtesy: “Darlington transistor” http://en.wikipedia.org/wiki/Darlington_transistor, 2012
6.4. Traffic Lights Circuit
The traffic light circuit is a simple circuit containing 9 LEDs.
40
i. One red LED for the red signal of the traffic light.
ii. One amber LED for the amber signal of the traffic light.
iii. One green LED for the green signal of the traffic light.
iv. A group of 6 white LEDs form an arrow (shown in Figure 6.5) to show the „right-
turn‟.
This circuit consists of 5 connectors. Four connectors for each LED making connection to
the collector pins of the ULN 2803A through the 220 ohm resistor. The fifth connector
connects the anode of the LED with the 5V DC supply to each LEDs.
There are total 8 traffic lights circuits installed in the system. Two traffic light circuits are
installed for each direction. These circuits are connected to the collector of the ULN
2803A on the control interface with the help of connectors.
Figure 6.5 PCB Design of Traffic Light Circuit
6.5. Tools Used for Simulation
6.5.1. Proteus VSM
The Proteus VSM combines mixed mode SPICE circuit simulation, animated components
and microcontroller models to facilitate co-simulation of complete microcontroller based
design. It is possible to develop and test such designs before a physical prototype is
constructed. This is possible because interaction with the design is possible using circuit
indicators like LED, display panels, actuators etc. It also provides extensive debugging
41
facility by employing breakpoints, single stepping and variable display of both assembly
code and high level language source code.
6.5.2. Livewire
Livewire is a CAD product developed by New Wave Concepts. It allows the user to create
and simulate electronic circuits using an extensive gallery of circuit components. Switches,
transistors, diodes, integrated circuits and hundreds of other components can all be
connected together to investigate the behaviour of a circuit. There are no limits to what can
be designed and any loose connections or faulty components to worry about.
The finished circuit can be exported into sister-program PCB Wizard in order to route it for
a PCB design.
6.6. Tools used for Circuit Design
6.6.1. PCB Wizard
The PCB Wizard is a powerful and a highly innovative package for designing single side
and double side PCBs. It provides a comprehensive range of tools covering all the
traditional steps in PCB production, including schematic drawing, schematic capture,
component placement, automatic routing. Circuit diagrams are drawn using circuit symbol
components connected with wires. Components are simply dragged, dropped and arranged
in the working window and necessary connections are made between the component
terminals.
6.6.2. KiCAD
KiCAD is an open source software suite for electronic design automation. It facilitates the
design of schematics for electronic circuits and their conversion to PCB designs. KiCAD
uses an integrated environment for all of the stages of the design process – schematic
capture, PCB layout, Gerber file generation/visualization and library editing. KiCAD has a
42
built-in basic auto router. Tools exist within the package to create artwork and 3D view of
the PCB. KiCAD is also a cross platform.
6.7. Tools used for Microcontroller Programming
6.7.1. MPLAB
MPLAB IDE is a program that runs on a PC to develop applications for microchips,
microcontrollers and digital signal controllers. The IDE provides a single integrated
environment to develop code for embedded microcontroller. The compiler associated with
the IDE compiles the high level C code and corresponding hex code is generated. The
compiler can optimize high level C code, eliminate code that will never be executed, share
common code fragments among multiple functions and can identify data and registers that
are used inefficiently optimizing their access. The MPLAB graphical user interface serves
as a single environment to write, compile and debug for embedded applications.
43
7. SOFTWARE IMPLEMENTATION AND SIMULATION
7.1. Programming Language and Tools
i. Language: C and C++
C is a general-purpose programming language initially developed by Dennis
Ritchie between 1969 and 1973 at Bell Labs. C++ is a statically typed, free-form,
multi-paradigm, compiled, general-purpose programming language.
Courtesy: “C (programming language)” http://en.wikipedia.org/wiki/C_(programming_language)
“C++” http://en.wikipedia.org/wiki/C%2B%2B
ii. Image Processing Library: OpenCV version 2.1 (details in Appendix D)
iii. IDE: Qt Creator version 2.0.1 has been used as the IDE. (details in Appendix E)
7.2. Simulation Software/GUI
The simulation software is more or less a GUI of the system developed in this project that
carries out the necessary image processing algorithms on the sequence of video frames of
the road traffic. Apart from this, this software also provides a user friendly environment
that facilitates the traffic surveillance as well as enables the operator or traffic personnel to
observe and control the functioning of the traffic lights system. The simulation software is
run on an IPU which may be situated at a remote location that need not be near to the
traffic junction being managed. However, it must be made sure that the video transmission
of the traffic junction to the IPU must be continuous and should not be disturbed by abrupt
power cuts or similar other problems.
The GUI is operated only by the authorized traffic personnel from the control room. As the
traffic personnel can view the real-time traffic situations of the traffic junction being
considered, he/she can switch from the automatic to the manual mode very easily. As
explained earlier, in manual mode the traffic lights can be manually controlled one traffic
light from each traffic light arrangement at a time. Thus, he/she can take necessary actions
to solve the traffic deadlock conditions or any other condition that cannot be solved by this
automated system such as prioritizing the ambulances, fire-engines or the vehicles of the
VIPs.
44
After the program is installed and run, the actual operation is begun after the “Start” button
as shown in Figure 7.1 has been clicked. The circles of each colour shown in the figure
represent the corresponding traffic lights.
Figure 7.1 Screenshot of the GUI
The program can be run in two modes – automatic and manual modes.
I) Automatic Mode
In automatic mode, the following features are available:
i. Fixed Timing System
In this system, the traffic lights are changed in certain standard order after every fixed
interval of time. This feature is applicable for automatic traffic control especially
during the morning and late night when there is less traffic density.
ii. Image Processing System
In this system, the traffic lights are changed in accordance with the density of vehicles
in each street which is determined by the DIP of the sequence of video frames of the
vehicle traffic of each road.
45
II) Manual Mode
In manual mode, the traffic personnel can alter the order in which the traffic lights are
illuminated under normal conditions so as to keep up with the existing traffic scenario.
This mode is executed under special conditions such as during the arrival of ambulances or
fire-engines in any lane that does not have the priority to be served at that instant.
The altered traffic light arrangements can take any one of the following eight states:
i. Green-Red-Red-Red
ii. Yellow-Red-Red-Red
iii. Red-Green-Red-Red
iv. Red-Yellow-Red-Red
v. Red-Red-Green-Red
vi. Red-Red-Yellow-Red
vii. Red-Red-Red-Green
viii. Red-Red-Red-Yellow
46
8. PROBLEM FACED AND SOLUTIONS
8.1. Some Solved Problems
8.1.1. Software
i. Background Subtraction
The conventional background subtraction method proved to be inefficient in case of
the video footages that are to be processed by the system developed in this project.
So, a different background subtraction method has been devised to overcome this
problem. This method exploited the feature of perspective distortion of the video
frames.
ii. Detection of Vehicles Far Away from the Traffic Junction
It was difficult to detect the vehicles that were far away from the traffic junction.
This problem is solved by considering the differential intensity values of the
adjacent pixels in the section of the video frame corresponding to the region
situated far away from the traffic junction.
8.1.2. Hardware
i. Data Transmission between the IPU and Control Interface
As the microcontroller PIC16F877A has been used, either serial or parallel
communication technique for data transmission could be implemented. Since the
serial communication is far better than the parallel communication, the data transfer
or transfer of control signals between the IPU (or PC) and the control interface of
this system is established by asynchronous serial communication.
ii. Loss in Data Transmitted
Since the serial communication is very prone to bit loss during data transmission,
the bits are sometimes lost during transmission which may generate wrong
information at the receiving end. This problem is solved by using the proper cable
which is verified through experimentation.
47
8.2. Some Unsolved Problems
8.2.1. Software
Bright Spots or Lines on the Road
The white spots or lines that have been painted by the Metropolitan Traffic Police Division
or Department of Road and Department of Transport Management present on the road
grow brighter after the dilation operation. As a result, they give rise to extremely distinct
blobs which are also detected as vehicles.
48
9. RESULT AND ANALYSIS
9.1. Experimental Setup
The simulation software is installed on an IPU to begin the process of vehicle detection.
The continuous video inputs of the road traffic are provided to the IPU. The power supply
to the interface module is properly connected.
9.2. Hardware Testing
The process of hardware testing has been done in several phases to ensure the proper
working of the embedded system.
9.2.1. Basic Circuit Testing
This phase of the hardware testing includes the following tests:
i. Short circuit test between Vcc and ground.
ii. Checking the polarity of the capacitors and diodes.
iii. Checking the output of the 7805 regulator.
iv. Connectivity testing.
v. Testing the response of microcontroller, MAX 232 and ULN 2803A.
vi. Testing the bar-graph and LEDs.
9.2.2. Testing the Fixed Time System
In this test, the hardware of the system developed in this project was simulated in the
Proteus VSM. The Proteus simulation and testing helps to know whether the concept of the
corresponding hardware is feasible to implement or not. Although it does not certainly
guarantee that circuit will work, it still gives an idea about the working of the system.
The program for the fixed time traffic system was written and then loaded in the
microcontroller. The control interface was tested with the help of this program. The
response of the system was observed in the bar-graph.
49
9.2.3. Testing the Traffic Light Circuit
The traffic light circuit was tested by connecting the connector connected to the anode of
LED (traffic signs) via 220 ohm resistors to 5V DC supply and by connecting the
connector connected to the cathode of the LED (traffic signs) to the ground. It was tested
whether the lights were turned on or not. If the light was not illuminated, then the problem
was searched. Generally, the problem was due to either the faulty LEDS or the lack of the
proper connection in the circuit.
Figure 9.1 Simulation of Traffic Light Circuit in Proteus
9.2.4. Connecting the Control Interface and Traffic Light Circuit
After successfully passing the tests of the above phases, the two basic units of this
embedded system were connected together and tested. The program for the fixed time
system was loaded in the microcontroller. The traffic light circuits were connected to the
control interface with the pins placed at the collector of the ULN 2083A. The system
response was observed and corrected in case of problems. The only problem in this phase
was that some of the traffic signs didn‟t respond as they should have. The reason was lack
of proper connection between the traffic light circuit and the control interface. The proper
connection was made between those circuits at the end of this phase.
50
9.2.5. Connecting Control Interface and PC
The control interface and PC were connected to each other with the help of the USB to RS
232 connector. The USB to serial communication was tested. The data was sent from the
PC to the control interface and the response was observed. The data was also sent form the
microcontroller to the PC and it was checked whether the data reached to the PC or not.
Figure 9.2 Simulation of Serial Communication Circuit in Proteus
9.2.6. Connecting All Three Units and Testing
This was the sixth and the last phase in the process of the hardware testing. In this phase,
all the three units of the system were connected to each other and tested. The data was sent
from PC to the microcontroller placed on the control interface via USB to RS232 cable
(figure in Appendix C). The response of the microcontroller was observed in the bar-graph
and the traffic light circuits. The feedback was also sent to the PC by the microcontroller.
This feedback gives the status of the embedded system. The feedback data were compared
to the data sent initially by the PC to make sure that the system was working properly. At
the end of this phase the proper health of the hardware of the system was verified.
51
9.3. Software Testing
For the purpose of software testing, as the actual video footages of the road traffic that are
captured by the CC camera of the traffic department were not available, we had to go to the
actual locations and get the required video footages.
The video footages were captured under favourable lighting conditions and during the
daytime. Most of the tests were performed by using the individual frames of the video
footages.
After debugging both the hardware and software of this system, the video footages of the
road traffic captured by us were evaluated. Certain frames of those video files were
processed individually to test the performance of the system. These video frames were
modified with respect to the lighting conditions and degree of noise before they were
subjected to testing. The figures – Figure 9.3 shows the one of such frames and Figure 9.4
shows the screenshot of GUI that shows the number of vehicles (approximately) present on
the road. The GUI shows that 47 vehicles are present in the frame of the video footage
shown by Figure 9.3.
Figure 9.3 A Frame Showing the Road Traffic
52
Figure 9.4 Section of GUI Showing the Number of Vehicles
Similarly, the vehicle count for three other roads was calculated. Then, by comparing the
number of vehicles counted and on the basis of the traffic engineering theory described in
section 3.1, the green phase for the each road is determined and the necessary control
signals are transmitted to the decision unit (microcontroller) via serial communication.
These signals are decoded to illuminate the traffic light interfaced in the control interface.
For the purpose of this project and considering the scenario of traffic system in Nepal, the
order of illuminating the traffic lights in this system is quite different from that described
in section 3.1.
53
10. COST ANALYSIS OF THE PROJECT
The total expenditure done in favour of the project right from its initiation upto its
completion has been summarized in following table:
S. No. Item Specification Rate (Rs.) Quantity Total (Rs.)
1 Hydrogen Peroxide - 20 2 40
2 DB9 Female 25 1 25
3 Multi-adapter - 150 1 150
4 ULN 2803 - 35 4 140
5 DC Socket - 15 1 15
6 USB Socket Female 15 1 15
7 Soldering Rod Bit 40 Watt 75 1 75
8 DC Soldering Rump - 275 1 275
9 Ribbon Wire - 25 2 50
10 Capacitors 1000 µF 15 2 30
11 Capacitors 100 µF 16 2 32
12 Capacitors 10 µF 4 3 12
13 Capacitors 0.1 µF 10 1 10
14 Push Button - 5 1 5
15 11.0592 MHz Crystal - 35 1 35
16 IC Base - 10 5 50
17 40-pin Base - 40 1 40
18 MAX 232 - 125 2 250
19 7805 - 15 1 15
20 4007 - 10 1 10
21 PCB(12"x12") - 700 2 1400
22 6-pin Relimate (I) - 20 32 640
23 Network Resistors - 10 6 60
25 Drill Bit 1mm - 20 2 40
26 PIC 16F877A - 500 2 1000
54
S. No. Item Specification Rate (Rs.) Quantity Total (Rs.)
27 10-pin Male Relimate - 20 10 200
28 Ribbon Cable 1 metre 100 10 1000
29 Resistor 10 k 0.7 150 105
30 Resistor 1 k 0.8 50 40
31 74 LS 153 - 80 2 160
32 LED - - 63 170
33 Conc. HCL 250 ml 500 1 500
34 Acetone 250 ml 300 1 300
35 Dip-switch - 200 2 400
36 Bar-graph - 300 2 600
37 Miscellaneous - 1500 1 1500
Grand Total 9389
Table 9.1 Cost Analysis of the Project
55
11. LIMITATIONS AND FUTURE ENHANCEMENTS
11.1. Limitations
Despite the fact that some of the problems found in the existing traffic management system
of Nepal have been successfully addressed in this project, there are still some limitations in
the system developed in this project. The limitations of this system are as follows:
i. The cameras used to shoot the road traffic should be placed at sufficiently higher
position than the average height of the heavy vehicles such as bus, truck, etc.
ii. The camera should be aligned in such a way that its direction of observation aligns
with the road being observed as far as possible.
iii. The developed system should be used to detect the vehicles in robust
environmental conditions.
iv. This system may not respond well under unfavourable lighting conditions such as
during gloomy days or in poorly illuminated environments.
v. This system is unsuitable for use during night time.
vi. This system doesn‟t make the exact count of the number of vehicles present but
gives a relative estimation of volume of the vehicles in the road.
vii. This system has been developed considering the crossroads to run straight from the
traffic junction with no changes in inclination. Hence, it may need to be modified
in order to be used for curved and inclined roads.
viii. The developed system is unsuitable to detect the ambulances or fire-engines that
are in emergency.
11.2. Future Enhancements
i. This system can be further developed to detect the ambulances or fire-engines on
the basis of their siren by integrating the audio processing mechanisms or by
integrating the RFID approach to the present system.
ii. All the transmissions in and out of the system developed in this project are wired.
Hence, a wireless traffic control system can be developed.
56
iii. Advanced image processing algorithms and libraries could be used so that the
system can be used efficiently even during unfavourable lighting conditions and
during the night time as well.
iv. Further improvements can be made so that the system can determine the exact
number of vehicles in the road.
v. This system can be developed to read the numbers on the number plates of the
vehicles and keep the record.
vi. Microcontroller can be replaced by Field Programmable Gate Array (FPGA) as it
has many advantages over microcontroller regarding the speed, number of I/O ports
and performance.
57
12. CONCLUSION
The project “Automatic Traffic Control System using Digital Image Processing” aims at
automating the traffic management system. Although this system is not unique, it is an
upgrade to the similar systems developed in earlier projects.
The system emphasizes on the DIP algorithms for the detection of vehicles in the road. By
integrating the DIP in software with the necessary hardware to control the traffic lights, the
required automatic traffic control system has been developed. During the process of
development, many research works and papers carried out in the respective fields were
thoroughly studied. Based on the results and conclusions provided by such papers, the
algorithms that best addressed the problem domain of this project were selected.
This project has not been tested in the actual field although the performance of the system
for the video footages of the actual field was evaluated and quite satisfactory results were
obtained. Hence, some modifications or changes might still be needed before it is tested in
the actual field. As it has been explained in the sections 11.1 and 11.2, there are still a few
limitations that are to be overcome and few other enhancements that can be made in this
system so that it can be effectively launched in the actual field.
58
13. REFERENCES
[1] N. S. Kumar, M. Saravanan and S. Jeevananthan, Microprocessors and
Microcontrollers, 5th
Edition, Oxford University Press, 2012
[2] G. Singh, Highway Engineering, 5th
Edition, Standard Publishers Distributors, 2006
[3] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd
Edition
[4] G. Bradski and A. Kaehler, O’ Reilly Learning OpenCV, 1st Edition, O‟Reilly Media,
Inc., 2008
[5] H. T. Thi, A Robust Traffic Surveillance System for Detecting and Tracking Vehicles
at Night-time, University of Technology, Sydney, Faculty of Engineering, Nov 21,
2007
[6] V. Dangi, A. Parab, K. Pawar and S. S. Rathod, Image Processing Based Intelligent
Traffic Controller, Electronics and Telecommunication Dept., Sardar Patel Institute
of Technology, Mumbai, India
[7] A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989.
[8] M. Bramberger, R. P. Pflugfelder, A. Maier, B. Rinner and B. Strobl, A Smart
Camera for Traffic Surveillance, Institute for Technical Informatics, Graz University
of Technology, Graz, Austria
[9] Mrs. S. Mehta, Fuzzy Control System for Controlling Traffic Lights, proceedings of
the International Multi Conference of Engineers and Computer Scientists 2008 Vol I
IMECS 2008, 19-21 March, 2008, Hong Kong
[10] I. Jain and B. Rani, Vehicle Detection Using Image Processing and Fuzzy Logic,
International Journal of Computer Science and Communication, Vol. 1, No. 2, July-
December 2010.
[11] Y. Liu and P. Payeur, Robust Image-based Detection of Activity for Traffic Control,
Vision, Imaging, Video and Audio Research Laboratory, School of Information
Technology and Engineering, University of Ottawa.
[12] E. de la Rocha and R. Palacios, Image Processing Algorithms for Detecting and
Counting Vehicles Waiting at a Traffic Light, Journal of Electronic Imaging,
October-December 2010
[13] S. Johnsen and A. Tews, Real Time Object Tracking and Classification using a Static
Camera, proceeding of the IEEE ICRA 2009, workshop on people detection and
tracking, Kobe, Japan, May 2009
59
[14] M. Sivabalakrishnan and Dr. D. Manjula, An Efficient Foreground Detection
Algorithm for Visual Surveillance System, International Journal of Computer Science
and Network Security, Vol-9, No. 5, May 2009.
[15] M. Fathy and M. Y. Siyal, A Window-Based Edge Detection Technique for
Measuring Road Traffic Parameters in Real-Time, Real-Time Imaging 1, pp. 297-
305, Academic Press, 1995.
[16] M. Fathy and M. Y. Siyal, An Image Detection Technique Based on Morphological
Edge Detection and Background Differencing for Real-Time Traffic Analysis, Pattern
Recognition Letters, Vol. 16 (1995), pp. 1321-1330.
[17] S. Umbaugh, Computer Vision and Image Processing, Prentice Hall, NJ, 1998
[18] “Dilation”
http://homepages.inf.ed.ac.uk/rbf/HIPR2/dilate.htm, 2012
[19] “Traffic light”
http://en.wikipedia.org/wiki/Traffic_light, 2012
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APPENDIX
Appendix A
Cycle Length
It is time taken for one complete sequence of signal indications. This time falls between 40
and 120 seconds. In order to reduce delay to standing traffic, short cycle lengths are
preferred. The cycle length is designed in such a way that the waiting traffic queues should
not become unnecessarily long.
Phase
It is a part of the cycle length allocated for specific movement.
Interval
The time for which signal continues to reflect the same coloured light in a cycle is called
interval.
61
Appendix B
Pin Diagram of PIC 16F877A
62
Appendix C
An interfacing standard called RS-232 standard allows compatibility among data
communication equipments made by various manufacturers. It is the most widely used
serial I/O interfacing standard.
The connector used is either DB-25 connector or DB-9 connector. In labeling, DB-25P
refers to the plug connector (male) and DB-25S is for the socket connector (female). The
maximum length of cable between the receiver and transmitter should not exceed 100 feet
and the maximum cable capacitance is 2500 Pico farad.
Terminal to Computer RS-232 Interface
Since this standard was set long before the advent of the Transistor–Transistor Logic
(TTL) logic family, its input and output voltage levels are not TTL compatible. The TTL
logic used by microcontroller is +12V for logic 0 and -12V for logic 1 while in RS-232 a
logic 1 is represented by -3V to -25V and a logic 0 is represented by +3V to +25V.
To connect any RS-232 to a microcontroller system, converters such as MAX232, also
called as line drivers, are needed to convert the TTL logic levels to the RS-232 voltage
levels and vice-versa. One advantage of MAX232 chips is that it uses a +5V power source
63
which is the same as the source voltage for the 8052. Thus, there is no need of dual power
supplies.
Pin Configuration of MAX232
USB to RS232 Cable
64
Appendix D
Pin Diagram of ULN2803A
65
Appendix E
OpenCV (Open Source Computer Vision Library) is a library of programming functions
mainly aimed at real-time computer vision, developed by Intel, and now supported by
Willow Garage and Itseez. It is free for use under the open source BSD license. The library
is cross-platform. It focuses mainly on real-time image processing. If the library finds
Intel's Integrated Performance Primitives on the system, it will use these proprietary
optimized routines to accelerate itself.
OpenCV's application areas include:
i. 2D and 3D feature toolkits
ii. Egomotion estimation
iii. Facial recognition system
iv. Gesture recognition
v. Human–computer interaction (HCI)
vi. Mobile robotics
vii. Motion understanding
viii. Object identification
ix. Segmentation and Recognition
x. Stereopsis Stereo vision: depth perception from 2 cameras
xi. Structure from motion (SFM)
xii. Motion tracking
The library was originally written in C and this C interface makes OpenCV portable to
some specific platforms such as digital signal processors. Wrappers for languages such as
C#, Ch, Python, Ruby and Java (using JavaCV) have been developed to encourage
adoption by a wider audience.
However, since version 2.0, OpenCV includes both its traditional C interface as well as a
new C++ interface. This new interface seeks to reduce the number of lines of code
necessary to code up vision functionality as well as reduce common programming errors
such as memory leaks (through automatic data allocation and deallocation) that can arise
when using OpenCV in C. Most of the new developments and algorithms in OpenCV are
66
now developed in the C++ interface. Unfortunately, it is much more difficult to provide
wrappers in other languages to C++ code as opposed to C code; therefore the other
language wrappers are generally lacking some of the newer OpenCV 2.0 features. A
CUDA-based GPU interface has been in progress since September 2010.
OpenCV runs on Windows, Android, Maemo, FreeBSD, OpenBSD, iOS, Linux and Mac
OS. The user can get official releases from SourceForge, or take the current snapshot under
SVN from there. OpenCV uses CMake.
Courtesy: “OpenCV” http://en.wikipedia.org/wiki/OpenCV, 2012; “OpenCV”
http://opencv.willowgarage.com/wiki/
67
Appendix F
Qt Creator is a cross platform C++ IDE of QtSDK. It includes a visual debugger and an
integrated GUI layout and forms designer. The editor's features include syntax highlighting
and code auto completion. Qt Creator can be used for making GUI application with the use
of known programming language that is C++.
Qt Supports external library such as QExtSerial Port for reading and writing data from
Serial Ports. Qt also support Open CV library which has been used for DIP applications.
Also it supports QWT library for plotting and animation purposes. Qt has rich sets of in-
built library for GUI application.
Main Reasons to choose QT Creator:
i. It is well documented
ii. It runs in Windows and Linux
iii. It supports Open CV library.
iv. Its online help is available in QT Forums.
Qt allows execution of SQL language. Qt Creator has rich sets of virtual functions and
derived classes which is the core part of object oriented programming.
The main feature that makes Qt Creator different from other IDE such as Microsoft Visual
Studio is that it works on Signal and Slot mechanism. Signals and slots are used for
communication between objects. The signal and slot mechanism is central feature of Qt
Creator. Signal is emitted when particular event occurs and slot is the public or private
function. Hence, objects communicate with each other and functions with signal and slot
mechanism.
Courtesy: “Qt Creator” http://en.wikipedia.org/wiki/Qt_Creator, 2012; “Qt Creator”
http://qt.digia.com/product/developer-tools/, 2012
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Appendix F
Control Interface Circuit
Traffic Lights Circuit
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Appendix G
Contents of CD are as follows:
- Softcopy of the report of final year project
- Softcopy of the presentation of final year project
- Necessary datasheets of the ICs used in the hardware implementation
70