VEHICLE DETECTION AND COUNTING FROM UNMANNED AERIAL VEHICLE (UAV)
ANG KUAN KEE
BORANG PENGESAHAN STATUS TESIS
JUDUL: VEHICLE DETECTION AND COUNTING FROM UNMANNED
AERIAL VEHICLE SESI PENGAJIAN: 2014/2015
Saya ANG KUAN KEE
(HURUF BESAR)
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah) ini disimpan di Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dengan syarat-syarat kegunaan seperti berikut:
1. Tesis dan projek adalah hakmilik Universiti Teknikal Malaysia Melaka.
2. Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dibenarkan membuat salinan untuk tujuan pengajian sahaja.
3. Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi.
4. ** Sila tandakan (/)
______ SULIT (Mengandungi maklumat yang berdarjah
keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972)
_______ TERHAD (Mengandungi maklumat TERHAD yang
telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)
______ TIDAK TERHAD
__________________________ ____________________________
(TANDATANGAN PENULIS) (TANDATANGAN PENYELIA)
Alamat tetap: 128, Jalan Batu Unjur6, DR. ASMALA BIN AHMAD
Taman Bayu Perdana, 41200 Klang Nama Penyelia
Selangor
Tarikh: ______________ Tarikh: ____________
CATATAN: * Tesis dimaksudkan sebagai Laporan Akhir Projek Sarjana Muda (PSM)
VEHICLE (UAV)
ANG KUAN KEE
This report is submitted in partial fulfilment of the requirements for the Bachelor of Computer Science (Artificial Intelligence)
FACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY UNIVERSITI TEKNIKAL MALAYSIA MELAKA
DECLARATION
I hereby declare that this project report entitled
VEHICLE DETECTION AND COUNTING FROM UNMANNED AERIAL VEHICLE (UAV)
is written by me and is my own effort and that no part has been plagiarized without citations.
STUDENT :___________________________________ Date:_______
(ANG KUAN KEE)
SUPERVISOR :___________________________________ Date:_______
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
CHAPTER SUBJECT PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
ABTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
CHAPTER 1 INTRODUCTION
1.1 Introduction 1
1.2 Problem Statement 3
1.3 Objective 4
1.4 Scope 4
1.5 Project Significance 5
1.6 Expected Output 6
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction 7
2.2 Type of Vehicle 8
2.2.1 Statistic of Vehicle 10
2.3 Remote Sensing Interest 11
2.3.1 Review of Remote Sensing 12
2.3.2 Unmanned Aerial Vehicle Images (UAV images) 13
2.3.3 Detection and Classification for Efficient
Traffic Management 15
2.4 Unsupervised Learning 18
2.4.1 K-Means 19
2.4.2 ISODATA 21
2.5 Analysis 23
2.5.1 Problem Analysis 24
2.5.2 Data Requirement 24
2.5.3 Software Requirement 25
2.5.4 Ancillary Data Requirement 25
2.6 Conclusion 27
CHAPTER 3 METHODOLOGY AND DESIGN
3.1 Introduction 28
3.2 Phases 29
3.2.1 Phase One – Analysis and Preliminary Study 30
3.2.2 Phase Two – Design 30
3.2.3 Phase Three – Experimental and Results 31
3.2.4 Phase Four – Evaluation / Testing 31
3.4.1 Input 35
3.4.2 Unsupervised Classification 35
3.4.3 Selecting ROI for Vehicle Detection 36
3.4.4 Non – Vehicle Detection 36
3.4.5 Subset / Resize 37
3.4.6 Selecting Region for Inter Class of 37
Vehicle Detection 37
3.4.7 Calculate the Number of Pixel for
Vehicle Detection 37
3.4.8 Output 38
3.4.9 Accuracy Assessment / Testing,
Compare and Analysis 38
3.5 Conclusion 38
CHAPTER 4 IMPLEMENTATION AND RESULTS
4.1 Introduction 39
4.2 Data Pre – Processing 40
4.3 Input 40
4.4 Unsupervised Classification 41
4.5 Selecting Area for Vehicle Detection 45
4.6 Non – Vehicle Detection 46
4.7 Region Resizing 49
4.8 Selecting Region for Inter Class of
4.9 Calculate the Number of Pixel for
Vehicle Detection 55
4.10 Output 55
4.11 Accuracy Assessment / Testing,
Compare and Analysis 62
4.12 Conclusion 64
CHAPTER 5 ACCURACY ASSESMENT/ TESTING
5.1 Introduction 65
5.2 Samples for Testing Experiment 66
5.3 Comparison Analysis 67
5.4 Conclusion 69
CHAPTER 6 CONCLUSION
6.1 Introduction 70
6.2 Strengths 70
6.3 Weakness 72
6.4 Suggestion for Improvement 72
6.5 Conclusion 73
LIST OF TABLES
TABLE TITLE PAGE
2.1 UAV models 14
3.1 Schedule and Milestone of Final Year Project 33
3.2 Selection of ROI 36
4.5 Percentage of Number of Pixel for Every Category
and Classifiers 45
4.8.1 Number of Pixel for Single Vehicle of K-Mean 52
4.8.2 Number of Pixel for Single Vehicle of ISODATA 53
4.11 Accuracy Assessment of Output 61
5.3 Accuracy Assessment of 3 samples of data 68
6.1 Accuracy Assessment of Output 71
LIST OF FIGURES
FIGURE TITLE PAGE
2.1 Sales market of vehicle in 2012 to 2013 10
2.2 Sales and production report in December of 2013 and
December of 2014 11
2.3 Electromagnetic Spectrum (Department of Army, Feb 2014) 12
2.4 Flow Chart of simple iteration of K-means algorithm 20
2.5 Process of filter by K-Means 20
2.6 ISODATA algorithm by A.Dermanis, L.Biagi
According Richards et al. (2006) 22
2.7 Sample of Low Category of Traffic Road Map 25
2.8 Sample of Medium Category of Traffic Road Map 26
2.9 Sample of High Category of Traffic Road Map 26
3.1 Methodology design of the project 29
3.2 Flow Chart for Vehicle Detection and Classification 34
4.3.1 Low category 40
4.3.2 Medium category 40
4.3.3 High category 40
4.4.1 Classifier of K-Mean in Low category 41
4.4.2 Classifier of K-Mean in Medium category 42
4.4.3 Classifier of K-Mean in High category 42
4.6.1 Non-vehicle for Low category of K-Mean 46
4.6.2 Non-vehicle for Medium category of K-Mean 47
4.6.3 Non-vehicle for High category of K-Mean 47
4.6.4 Non-vehicle for Low category of ISODATA 47
4.6.5 Non-vehicle for Medium category of ISODATA 48
4.6.6 Non-vehicle for High category of ISODATA 48
4.7.1 Low category of K-Mean Resize 49
4.7.2 Medium category of K-Mean Resize 49
4.7.3 High category of K-Mean Resize 50
4.7.4 Low category of ISODATA Resize 50
4.7.5 Medium category of ISODATA Resize 51
4.7.6 High category of ISODATA Resize 51
4.10.1 Output of low category for ISODATA 55
4.10.2 Output of medium category for ISODATA 56
4.10.3 Output of high category for ISODATA 57
4.10.4 Output of low category for K-Mean 58
4.10.5 Output of medium category for K-Mean 59
4.10.6 Output of high category for K-Mean 60
5.2.1 Sample 1 65
5.2.2 Sample 2 65
5.2.3 Sample 3 65
5.3.1 Output of Sample 1 66
5.3.2 Output of Sample 2 66
CHAPTER I
INTRODUCTION
1.1. Introduction
Transportation has long become an essential means for people to travel from one place to another. Among all modes of transportation, land transportations have been known to have the most users due to their practicality and affordable cost. With the increase in the world population, the number of land transportations particularly car, bus and truck are also increasing drastically in all parts of the world. Although giving good impact to economy, failing to manage the traffic of these vehicles properly may cause a negative consequence, especially for developing countries like Malaysia.
Besides that, vehicle theft cases are rapidly increasing. Therefore, we need a system which can help us to detect stolen vehicles so finding back stolen can be carried out more effective. Therefore we need a more up-to-date approach to detect vehicles on the earth surface so that searching tasks can be done immediately and effectively.
This project aims to detect and count vehicles from unmanned aerial vehicle (UAV) imagery. The result obtainedcan greatly help authorised agencies in traffic management. By knowing the amount and location of vehicles, the relevant authorised agencies can can update the latest and accurate information to motorists so that they can plan their journey in a more efficient way. They will have more alternative way to prevent situations such as traffic jam, accident and other. Besides that, the traffic information enable motorists to plan their road trip and arrive the destination on the right time and safety to prevent being trapped in heavy traffic.
In carrying this project, image processing techniques are used to detect and count the number of vehicles, which include classification, enhancement and thresholding.
Shi et al. (2012) reported that, UAVs are better than traditional manned aircraft or other remote sensing operator. The reason they stated that is UAVs are no require any high cost platform and it can reduce the error or effect come by human instead of automate to remote it and sensing control itself. Besides that, UAVs also enable produce high resolution images and given accurate data result by the navigation sensor. Therefore, it can give an effective supplement work. So that, it have the strength to detect alter between object of image over the time.
1.2. Problem statement
This project embarks on the following objectives:
1. To identify methods for car detection and counting from UAV.
2. To propose suitable framework of image processing techniques for car
detection and counting from UAV.
3. To evaluate the performance of the techniques.
1.4. Scope
In the study, I divide the scopes into four parts which are software scope, data scope, area of study scope and user scope.
i. Software Scope
In this project, it will mainly use two software tools to complete it. There are ENVI 4.5 toolbox and Matlab R2010a. ENVI toolbox is for purpose to process the image, classified the object and analysis the imagery. However for the Matlab toolbox just as an assistance tool to complete the task after generate the output from ENVI toolbox. Besides that, it also will useful in analysis part the result.
ii. Data Scope
will be more precious. Furthermore, our data scope only had done the detection on certain intensity value only. Due to UAVs image is RBG, therefore in this project it will focusing on certain colour of vehicle.
iii. Area of Study Scope
Malacca was chosen as my focus area of study scope. Malacca is a famous state which also named as historical state. Therefore every year, season, they also successful attract thousands of visitor come from everywhere. So that, traffic jam will be always happen especially on certain peak hour or festival such as working hour or public holiday. Therefore, the traffic situations on Malacca state are requires more emphasize than other place.
iv. User Scope
The significance will be bring out from this project is useful for the researcher. The researcher can through the output as a reference material for them to further this project and enhance it to be better.
1.5. Project Significance
The expected result from this project is a processed image can provide the information regarding vehicle location and amount. This will greatly assist authorized parties in getting information regarding traffic situation on particular road and convey it to traffic management to do some improvement work or update latest news to the motorists. In addition, it also brings a lot of convenience for the particular relevant unit to identify or track certain vehicle on the particular road. This will be very useful especially for the police when they are requiring tracing somebody or some cases.
1.7. Conclusion
CHAPTER II
LITERATURE REVIEW
2.1 Introduction
This chapter reviews studies and information related to vehicle detection and counting. Literature review acts as a primary reference towards completion of this project. Discussion on previous research which is related to the project topic is discussed in detail. Literature review also is an important to prove that project is possible to be carried out. In my project, literature review are as a reference for me to ensure my project are work in the scope without go to digress. Besides that, through the review, it brings out a background for my project to propose a suitable technique in my project. Therefore, it assisted me in writing the analysis to get the right view when developing the project.
pure original sources are important. That is because it will be as a guidance of my project, therefore in my project I just will reference the most recently paper to avoid some conflict.
2.2 Type of vehicle
This project concerns on detection of few certain types of vehicle only. In general, vehicles can be categorised as below.
Heavy vehicle are differentiate into many class, there are:
