ii

TRAFFIC INTENSITY MONITORING USING

MULTIPLE OBJECT DETECTION WITH TRAFFIC

SURVEILLANCE CAMERA

BY

MUHAMMAD HAMDAN BIN HASAN GANI

A thesis submitted in fulfillment of the requirement for the

degree of Master of Science (Computer and Information

Engineering)

Kulliyyah of Engineering

International Islamic University Malaysia

JANUARY 2018

iii

ABSTRACT

Roads in Malaysia are getting more congested every day and the problem does not

seem to have a solution. Many researches are done to reduce the traffic congestion

however there are no practical or real-time solution to the problem. Most researches

are only able to simulate the traffic or rather generalize the pattern into simpler arrival

models, however this will not reflect on the actual road conditions. In this research, an

alternative approach to measure the traffic intensity on the road is discussed. With

computer vision has become a key business element in many corporations and the rise

in the technology of cameras with lower costs of processing has enabled us to develop

on most advanced system for many applications. Using traffic surveillance camera

placed on roads and object detection algorithm, a new method of calculating the traffic

intensity is developed and tested. Results of the test show the accuracy of about 80%

for the algorithm to be able to tell the difference between number of cars and

motorcycles. With this information, the road condition is estimated with higher

accuracy. The result and performance are tabulated and some of the limitations are

discussed in detail in the last chapter. However, there is still a lot of work need to be

done until the application can run accurately in real-time.

iv

البحث خلاصة

تشهد الطرق في ماليزيا ازدحاما يتزايد يوميا, ولا يبدو أن هذه المسألة ستحل على المدى القريب. العديد من

الأبحاث قد أجريت للحد من هذا الإزدحام المروري ولكن ليس هناك أي من التطبيقات الواقعية التي قد تقضي

ى المشكلة. معظم هذه الأبحاث قامت فقط بمحاكاة النظام المروري أوفقط أنتجت تعميما مبسطا لهذا النموذج , عل

دون أن تعكس أحوال المرور واقعيا. هذا البحث يقدم و يناقش مخططا بديلا لقياس كثافة حركة المرور . مع

مة رئيسية للعديد من الأعمال في كثيرمن التقدم الذي يشهده العلم اليوم حيث أصبحت رؤية الكمبيوتر دعا

الشركات إلى جانب االتقدم في تكنولوجيا الات التصوير بأقل التكاليف , أصبح بالامكان تطويرأنظمة معقدة لشتى

التطبيقات. استعمال كاميرات مراقبة حركة المرور التي توضع على جانب الطرق المجهزة بخورازميات الكشف

أدى إلى إمكانية تطبيق طريقة جديدة في حساب كثافة حركة المرور و التي تم تطويرها عن الأجسام الصلبة

% و التي تستطيع التمييز بين 80واختبارها في هذا البحث. نتائج الاختبارات لهذه الطريقة تصل إلى دقة تقارب

الطرق بدقة عالية. نتائج نوع وعدد السيارات و الدراجات النارية. بواسطة هذه المعلومات يمكن تقدير أحوال

وأداء هذه الطريقة المقدمة تم عرضها و مناقشتها تفصيليا مع استعراض عيوبها في الجزء الأخير من هذا

البحث . وعلى كذالك فإن كثيرا من العمل يجب أن ينجز حتى تصل هذه الطريقة إلى مرحلة التطبيق الفعلي آنيا

وبدقة .

v

APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion, it conforms

to acceptable standards of scholarly presentation and is fully adequate, in scope and

quality, as a thesis for the degree of Master of Science (Computer and Information

Engineering)

…………………………………..

Othman Omran Khalifa

Supervisor

…………………………………..

Teddy Surya Gunawan

Co-Supervisor

I certify that I have read this study and that in my opinion it conforms to acceptable

standards of scholarly presentation and is fully adequate, in scope and quality, as a

thesis for the degree of Master of Science (Computer and Information Engineering)

…………………………………..

Rini Akmeliawati

…………………………………..

Belal Ahmed Hamida

This thesis was submitted to the Department of Electrical and Computer Engineering

and is accepted as a fulfilment of the requirement for the degree of Master of Science

(Computer and Information Engineering)

…………………………………..

Anis Nurshikin bt Nordin

Head,

Department of Electrical and

Computer Engineering

This thesis was submitted to the Kulliyyah of Engineering and is accepted as a

fulfilment of the requirement for the degree of Master of Science (Computer and

Information Engineering)

…………………………………..

Erry Yulian Triblas Adesta

Dean, Kulliyyah of Engineering

vi

DECLARATION

I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Muhammad Hamdan bin Hasan Gani

Signature ........................................................... Date .........................................

vii

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF

FAIR USE OF UNPUBLISHED RESEARCH

TRAFFIC INTENSITY MONITORING USING MULTIPLE OBJECT

DETECTION WITH TRAFFIC SURVEILLANCE CAMERA

I declare that the copyright holders of this dissertation are jointly owned by the student and

IIUM.

Copyright © 2018 Muhammad Hamdan bin Hasan Gani and International Islamic University Malaysia. All

rights reserved.

No part of this unpublished research may be reproduced, stored in a retrieval system, or

transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or

otherwise without prior written permission of the copyright holder except as provided below

1. Any material contained in or derived from this unpublished research may be used by

others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print or electronic)

for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieved system and supply

copies of this unpublished research if requested by other universities and research

libraries.

By signing this form, I acknowledged that I have read and understand the IIUM Intellectual

Property Right and Commercialization policy.

Affirmed by Muhammad Hamdan bin Hasan Gani

……..…………………….. ………………………..

Signature Date

viii

ACKNOWLEDGEMENTS

Firstly, I would like to thank my family and close friends who helped me financially

and morally throughout my research and being there when I needed them the most.

I would also like to thank everyone involved in my research especially my lecturer

Professor Othman and my co-supervisor Dr Teddy who have guided me throughout

the research as well as advising me in choices I had to make in life.

Furthermore, I would like to thank the University for providing their resources and

guidance from all other lecturers from my Kuliyyah staff for their effort in my

research. I will forever be grateful for everyone’s effort. Amin

ix

1. CONTENTS

Abstract ......................................................................................................................... iii

iv .......................................................................................................................البحث خلاصة

Approval page ................................................................................................................ v

Declaration .................................................................................................................... vi

Acknowledgements ..................................................................................................... viii

1 Contents ................................................................................................................. ix

List of Table .................................................................................................................. xi

List of Figures .............................................................................................................. xii

CHAPTER ONE ............................................................................................................ 1

1. Introduction ............................................................................................................ 1

1.1 Background of Study ....................................................................................... 1

1.2 Brief Introduction to Traffic Modelling .......................................................... 3

1.3 Statement of problem ...................................................................................... 5

1.4 Research Objectives ........................................................................................ 7

1.5 Research Scope ............................................................................................... 7

1.6 Research Methodology .................................................................................... 9

1.7 Thesis Overview ............................................................................................ 10

CHAPTER TWO ......................................................................................................... 12

2 Literature Review ................................................................................................. 12

2.1 Introduction ................................................................................................... 12

2.2 Object Detection and Tracking Techniques .................................................. 12

2.3 Traffic monitoring Techniques...................................................................... 21

2.4 Traffic Modelling Techniques ....................................................................... 25

2.4.1 Modelling Techniques ........................................................................... 25

2.4.2 Space based Modelling .......................................................................... 26

2.4.3 Time based modelling ............................................................................ 28

2.4.4 Machine Learning .................................................................................. 29

2.5 Image Capturing Techniques ........................................................................ 30

2.6 Artificial neural network ............................................................................... 31

2.7 Summary ....................................................................................................... 34

CHAPTER THREE ..................................................................................................... 35

x

3 Methodology and Implementation........................................................................ 35

3.1 Introduction ................................................................................................... 35

3.2 Data Acquisition ............................................................................................ 35

3.2.1 Camera location ..................................................................................... 35

3.2.2 Computer Configuration ........................................................................ 37

3.3 Overview of the Implementation................................................................... 37

3.4 Object Classification using Neural Network ................................................. 38

3.5 Video Capturing and Segmentation .............................................................. 41

3.5.1 Load Video Capture Device and Select ROI ......................................... 42

3.5.2 Blob analysis and Feature detection ...................................................... 43

3.5.3 Display Detection Result ....................................................................... 45

3.6 Estimating the Traffic Intensity..................................................................... 46

3.7 Summary ....................................................................................................... 48

CHAPTER FOUR ........................................................................................................ 49

4 Result and Discussion ........................................................................................... 49

4.1 Introduction ................................................................................................... 49

4.2 Discussion ..................................................................................................... 49

4.2.1 Accuracy of program ............................................................................. 49

4.2.2 Traffic intensity ...................................................................................... 55

4.2.3 Performance ........................................................................................... 58

4.2.4 Limitation ............................................................................................... 59

4.3 Final Implementation .................................................................................... 60

CHAPTER FIVE ......................................................................................................... 61

5 Conclusion ............................................................................................................ 61

5.1 Conclusion ..................................................................................................... 61

5.2 Research Contribution ................................................................................... 62

5.3 Future works .................................................................................................. 62

6 References ............................................................................................................ 63

7 Appendix A........................................................................................................... 67

8 Appendix B ……………………………………………………………………...74

9 Appendix C………………………………………………………………………87

xi

LIST OF TABLE

Table No. Page No.

Table 2.1 Summary of the related work....................................................................... 24

Table 2.2 Summary of section 2.4 to 2.6 ..................................................................... 33

Table 3.1 Market Share of Manufacturers in Malaysia ............................................... 46

Table 3.2 Average Vehicle Length in Malaysia .......................................................... 47

Table 4.1 Estimated Accuracy using 3 sample datasets .............................................. 50

Table 4.2 Estimated Accuracy using 4 random sample datasets ................................. 51

Table 4.3 Actual Accuracy of the detected vehicles. ................................................... 51

Table 4.4 Accuracy of the program in day and night .................................................. 54

Table 4.5 Number of cars and traffic intensity in low congestion during the day ....... 56

Table 4.6 Number of cars and traffic intensity during congestion during the day ...... 57

Table 4.7 Average processing time of different dataset sizes ...................................... 59

xii

LIST OF FIGURES

Figure No. Page No.

Figure 1.1 Typical Highway in Malaysia (Source: http://www.thenutgraph.com/buy-

back-of-privatised-highway-concessions-more-cost-effective/) .................................... 3

Figure 1.2 The location of the tracking window ............................................................ 4

Figure 1.3 Flowchart of the Whole Research ................................................................ 9

Figure 2.1 Plus Malaysia Traffic Control Centre (Plus Traffic, 2016) ........................ 14

Figure 2.2 Currently available traffic monitoring solutions (Left:GoogleMaps, Right:

Waze) ........................................................................................................................... 14

Figure 2.3 Overlapping tracking object in single image. (Source :

http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/) .............. 16

Figure 2.4 Image processing using background subtraction from frames(Source:

http://www.instructables.com/id/Subtracting-the-background-from-a-video-with-

Intel/) ............................................................................................................................ 18

Figure 2.5 Human activity tracking using computer vision(Source:

https://www.andol.me/1862/a-review-of-people-counting-using-opencv-part-2/) ...... 19

Figure 2.6 Macroscopic Urban Traffic Model plotted (Source :

https://www.researchgate.net/figure/282890606_fig8_Macroscopic-fundamental-

diagram-for-urban-traffic-networks) ............................................................................ 21

Figure 2.7 Link equations and its variables used in (Yan et al., 2014) ........................ 26

Figure 2.8- Flow density using G/M/1 from Boulmakoul ........................................... 28

Figure 2.9 Supervised and Unsupervised Learning model (Left), Reinforcement model

(right) ........................................................................................................................... 32

Figure 3.1 location of Data Collection ......................................................................... 36

xiii

Figure 3.2 Placement of Camera above the road ......................................................... 37

Figure 3.3 Overview of the Implementation ................................................................ 38

Figure 3.4 Image Classification using bag of features ................................................. 39

Figure 3.5 Loading image sample into workspace ...................................................... 40

Figure 3.6 Determining the training and validation frames ......................................... 40

Figure 3.7 Generation the Confidence matrix .............................................................. 41

Figure 3.8 Overall process for identifying vehicle to measure traffic intensity .......... 42

Figure 3.9 Loading and displaying the video............................................................... 43

Figure 3.10 Identifying the ROI................................................................................... 43

Figure 3.11 Blob analysis and noise reduction ............................................................ 44

Figure 3.12 ROI sent for Classification and Output is added ...................................... 45

Figure 3.13 Display the result of ROI .......................................................................... 45

Figure 3.14 Running the traffic intensity algorithm .................................................... 46

Figure 3.15 Flowchart of traffic intensity algorithm ................................................... 48

Figure 4.1 Birds and other natural occurring errors blocking the camera (left).Large

sized bus blocking 2 cars view (right). A Backhoe produces a false positives (center)

...................................................................................................................................... 52

Figure 4.2 Positive detection of a car........................................................................... 53

Figure 4.3 False positive where a motorcycle is detected as a car and a motorcycle .. 53

Figure 4.4 Lighting condition during day (top) and night (bottom) ............................ 54

Figure 4.5 Traffic intensity during low congestions .................................................... 56

Figure 4.6 Traffic intensity during congestion present ................................................ 57

Figure 4.7 Comparison of traffic intensity between link congestions ......................... 58

Figure 4.8 The final GUI from the MATLAB code .................................................... 60

1

CHAPTER ONE

1. INTRODUCTION

1.1 BACKGROUND OF STUDY

In the modern world, the rise in the number of cars, buses and other modes of

transportation has led to increase in frequency of traffic congestion across the world.

Uncontrolled urban development and improper planning in infrastructure has

exponentially increased this problem where everyone using the public road system

will face the same issue of bad traffic congestion(Abdelfatah, Shah, & Puan, 2015).

Vision based traffic monitoring system is a good solution to address this problem(T.

Chen & Lu, 2016; Datondji, Dupuis, Subirats, & Vasseur, 2016). Furthermore, object

detection and tracking of multiple objects are important research done actively in the

computer vision field to aid on this problem.

New tools are necessary to monitor the traffic conditions in high density areas.

There are many researches written in the past regarding this issue but recent

developments in high speed internet and number of traffic cameras in the streets by

governing agencies has increased the need for more advanced traffic detection and

tracking for vehicles more important. Currently, these processes are done using the

control tower which has people in them to monitor the traffic conditions among all the

cameras located on the highways but this is no longer practical when the number of

cameras increase with the rise of the number of highways. Therefore, more resources

will be needed to monitor them unless some tool to monitor them automatically are

developed.

2

However, there are still a lot of problems using traffic surveillance camera for

vehicle detection. Some of the most critical problems are occlusion of object in field

of view, glaring from bright light of car’s headlights and taillights, weather influences

on lighting and so on. Different researchers have done significant research to increase

the accuracy of result using traditional approach however these methods still take a lot

of time for image processing. The unpredictability of the drivers also tends to make

tracking more difficult than it should be.

Another great challenge of using traffic surveillance camera is the accuracy of

measuring the four main criteria of true positives, true negatives, false positives and

false negatives. In a heavily congested traffic condition, it becomes highly unreliable

to use such techniques to measure the traffic intensity but in this research, neural

networks may be the key of solving this problem.

Current technology on identifying the road traffic intensity depends on data

from the users of the roads(P.T. Chen, Chen, & Qian, 2014). Many researches are also

done using data mining approach to sort out relevant data from the user’s mobile

phones and social networks uploads to map out the road conditions in real time.

Applications such as Waze and Google maps uses GPS location data from its users to

map certain road conditions and also to predict future patterns using machine

learning(Graves et al., 2016). This method is a good platform for this application.

However, this approach relies heavily on the user data and network intensive for

typical usage.

A robust system that not only identifies the traffic intensity but the vehicle on it

is also necessary to create future databases of vehicles accessing the roads(Flores,

Mata, Fernandez, Aliane, & Puertas, 2014)(Yeh, Zhong, Member, & Yue,

2017)(Elleuch, Wali, & Alimi, 2015). By predicting the vehicle types and numbers, a

3

restriction policy can be enforced to reduce the congestion by limiting heavy vehicle

on certain time on the day and light vehicles on the other. Such practices are used in

some highways in Malaysia. The system should also be able to reroute traffic if the

roads are getting congested to reduce the overall congestion time. Incidents like traffic

accidents and road blocks are unexpected forms of danger that should also be

considered for this system to work efficiently.

1.2 BRIEF INTRODUCTION TO TRAFFIC MODELLING

Figure 1.1 Typical Highway in Malaysia (Source: http://www.thenutgraph.com/buy-

back-of-privatised-highway-concessions-more-cost-effective/)

Figure 1.1 shows a typical link on a road system in Malaysia. Most of the road system

employs a 2-way traffic flow in opposite directions. The input to the road is vehicles

and the output is also vehicle. Normally, there is no discrimination on the speed the

vehicles are supposed to be moving in the link. Traffic modelling technique involves

using Mathematical application to measure the parameters related to traffic.

These studies are extensively done by the London Traffic management for the

Mayor of London. (Blewitt & Smith, 2010) was part of the team that modelled the city

4

of London extensively with model including total travel time, congestion control

schemes, error handling approach as well as efforts to reduce carbon footprint.

However, the roads in all the cities across the world will have a different

approach to solve this problem as the traffic in these countries are treated uniquely.

Streets of Europe will have a different law compared to the ones in Asia such as the

speed limits, units of measurement metrics and so on. In chapter 2 of this thesis, the

modelling techniques from different perspectives will be discussed in detail however

the most basic problem to solve is counting the number of cars entering the link as

shown in figure 1.2.

Figure 1.2 The location of the tracking window

As vehicles move across the link, it will flow in single direction. The vehicles

on the right-hand side are monitored from window 1 to window 2 while the left-hand

side are tracked from window 2 to 1. When the vehicle is detected from the first

window, the object is initialized and counted while it moves along the road. A timer is

5

used to determine the time taken for the vehicle to reach last tracking window and the

number of vehicles are recorded.

The time measured between these windows and the number of vehicles are

variables used to determine the traffic intensity of the road. If the number of vehicle

on a certain time is high and the time taken for it to travel is long along with some

other parameters, then it can be concluded that the road is congested or not. Error may

occur in tracking all vehicles therefore a measure of accuracy is important in this

research.

1.3 STATEMENT OF PROBLEM

In recent years, the time spent by average people on the road has risen to an average of

spending about 32 hours per year on traffic congestion(Sabur, 2017) in europe while

Americans spent about 42 hours per year(Anderson, 2016). Stuck in traffic congestion

has become a routine in our daily lives but we cannot be living with this problem

anymore. With much advancement in autonomous driving and rapid development in

computer vision, the tools are readily available for us to solve this problem in the

future. However, there are some parameters in real time and space are not considered

in these solutions.

There is a real need for a completely autonomous system to handle traffic

congestions in Malaysia (Abdelfatah et al., 2015). Most algorithms developed by

plenty of researchers have not been able to detect traffic patterns in real world but

only track the movement of objects in general motion and predict their direction using

basic assumptions. A more realistic approach is needed to model these conditions

related to road management instead of treating it as a networking problem. Current

6

approach of using the Poisson Distribution used on the road traffic may be inaccurate

due to human action being indeterministic while driving on the road makes it harder

to predict the traffic flow based on predicted statistical data.

Furthermore, with the improvement in machine learning, the accuracy of data

can be significantly increased for training the detection of objects. Most algorithms

discussed in the next chapter focuses on using Haar Cascade for vehicle

detection(Hasan, Arif, Asif, & Raza, 2017)(Wu, Kang, & Zhuang, 2016). Though this

is a simpler process to handle, it takes a long time to train the cascade and the set of

images for training the module is always constant. However, using machine learning,

the accuracy can be improved by inputting the positive images back into training

sequence images. This approach is still lacking in many research currently as most

image processing is moving forward to hardware based acceleration using deep

learning.

Using multiple sensors are also an alternative way for vehicle monitoring.

However, these sensors are not able to provide enough information as accurate and

complete as using computer vision. Multiple researchers used this approach to map

traffic conditions such as wireless sensor network(Collotta, Pau, Salerno, & Scata,

2012)(Ito, Hirakawa, Sato, & Shibata, 2016) which incorporated sensors into cars to

monitor them but this will not only incur cost but add more privacy issue later when

implemented. Other research used RFIDs tagged in the cars and monitoring them with

high accuracy however, the infrastructure is not feasible for large scale application as

the data will be huge. This has shown a rise in need for traffic monitoring.

In current time, the status quo uses GPS data from the phones and social media

as well poses additional risk of privacy violation. Applications such as Waze, Here

maps and Google Maps are commonly used by people and network traffic is generated

7

by them to be sent to the servers. Multiple research and experiences in the past shown

that even the most secured systems are vulnerable to attackers and user information

are loss which justifies the need for alternative ways of acquiring data.

With most highways in Malaysia equipped with traffic cameras the solution

seems clear but currently vehicles are monitored using analog means such as control

rooms with people and traffic patrols. This is also a waste of resources such as

cameras not being used to their full potential and road repairs and assistance during an

emergency. With the rapid development in Computer Vision(CV) technology this

problem must be solved quickly.

Moreover, developing an automatic system that predict traffic flow may able

these governing bodies to develop new traffic models for implementing alternatives

options if there is a congestion or accident on the road without relying on the current

technology.

1.4 RESEARCH OBJECTIVES

The objective of this research as follows:

1. To develop a model that can analyse traffic pattern in real time.

2. To identify traffic intensity in a video sequence using MATLAB.

3. To evaluate the performance of the proposed system.

1.5 RESEARCH SCOPE

To simplify the research, and data collection, the following are the scopes of this

study:

1. A single camera monitoring the oncoming and ongoing traffic in 2 directions.

2. Only three scenarios are studied on the road for data collection namely the no

congestion and higher congestion.

8

3. The traffic camera will only record on the day and clear weather to remove

complexity to developing the program.

9

1.6 RESEARCH METHODOLOGY

To achieve the objective, the following flowchart is formulated to identify traffic

intensity.

Figure 1.3 Flowchart of the Whole Research

10

The flowchart in figure 1.3 shows the process taken for the entire project. The

research begins from reading the literature review of the subject matter to identify the

solutions currently being used by practical applications and their limitations. Multiple

solution is then weighed together to formulate a research problem for further study.

Then specific objectives are targeted for the entire research.

Next, a methodology is proposed to solve the problem identified in the last

process. This methodology will try to cover most of the problems in the research

objective and questions. A new framework will be proposed to be implemented in the

next process. The implementation will execute the proposed methodology to generate

data. These data are tabulated using data analysis tool in MATLAB and in Microsoft

Excel. A detailed overview of each process is explained in Chapter 3.

Finally, result of the research is discussed based on the data obtained in the

previous process. Key factors that effects the outcome of the research as well as

limitations of the study will be highlighted and discussed to formulate a conclusion.

Based on the conclusion, an extension of the study will be proposed.

1.7 THESIS OVERVIEW

In this research, the first chapter discusses on the fundamental concepts of the research

such as the research question, objectives and overview of the research in general. The

next chapter will focus on the literary works done on the research area. Most of the

related works are compared and a tabulated form used to summarize the materials.

This chapter will highlight on the issues related to traffic modelling techniques as well

as object detection and tracking systems with their algorithms.

11

The following chapter will explain the methodology used throughout the

research to obtain the data. All the key processes and assumptions used on this area

will be pointed out and the application outline is discussed comprehensively.

In Chapter 4, the results obtained in Chapter 3 for implementations are

explained and effects of the assumptions are identified. This chapter will also compare

the works done by previous related work and benchmark them to the research

outcome.

The last chapter will conclude the works done and formulate an extension that

can be done from this research area.

12

CHAPTER TWO

2. LITERATURE REVIEW

1.8 INTRODUCTION

In this chapter, the works related to the research area and the state of the current

research are discussed. The key limitations of current systems of multiple object

detection and tracking in Traffic Surveillance are identified. This chapter will also

provide an overview of the multiple road traffic modelling techniques currently being

used by authorities and researcher in real time. Some basic comparison of the

modelling schemes is presented and discussed further. Next, some basic object

detection systems assessed with some introduction into machine learning. Finally, the

chapter is concluded with a summary.

1.9 OBJECT DETECTION AND TRACKING TECHNIQUES

Research on visual tracking and computer vision are still developing as it has a wide

range of applications ranging from military to medical, security and surveillance.

However, image detection systems are only as accurate as their classifier because they

are only mapped on to objects from similar characteristics. However, this may not

apply to many objects from around the world. This issue has been actively researched

by many experts in different fields of study. There are different perceptions from

people from Civil Engineering background and Network Engineering background as

both are solving traffic flow in networked infrastructure.