Vehicle Detection And Counting From Unmanned Aerial Vehicle (UAV).
VEHICLE DETECTION AND COUNTING FROM UNMANNED AERIAL
VEHICLE (UAV)
ANG KUAN KEE
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
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
_______ TERHAD
(Mengandungi maklumat yang berdarjah
keselamatan atau kepentingan Malaysia
seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
(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,
Taman Bayu Perdana, 41200 Klang
Selangor
Tarikh: ______________
DR. ASMALA BIN AHMAD
Nama Penyelia
Tarikh: ____________
CATATAN: * Tesis dimaksudkan sebagai Laporan Akhir Projek Sarjana Muda
(PSM)
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihakberkuasa
VEHICLE DETECTION AND COUNTING FROM UNMANNED AERIAL
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
2015
ii
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:_______
(DR. ASMALA BIN AHMAD)
iii
DEDICATION
I would like to dedicate my final year project report to my beloved family, friends
and my lecturers. For my supervisor, Dr. Asmala Ahmad and my evaluator, Dr
Sharifah Sakinah, always guiding, comment and helping me to complete up this
project. Besides that, family is the one always give me spirit support and life-long
love to me. They have been very supportive and encouraging in completion of my
thesis and throughout the years of my studies. Furthermore I also would like to
dedicate to the friends who have been supportive and helpings throughout the project
development.
iv
ACKNOWLEDGEMENT
I would like to take an opportunity to express my gratitude to those who contributed
and showed their kindness in helping and assisting me to complete this project. First
and foremost, I would like to thank my supervisor, Dr. Asmala Ahmad and my
evaluator, Dr. Sharifah Sakinah for all the guidance and encouragement. I am indebt
for the patient, support and motivation from the beginning to the end of my project.
Thank you also for instructing and teaching me in developing the understanding of
the project and eventually me enable to grasp the essence of this project. Besides that,
I also like to thank a PhD student, Mr. Nasruddin Abu Sari whom provided me with
the UAV data for this project. Most important, I am thankful to my family whom
continuously keeping up my spirit and supporting me in term of financial and moral.
They keep on encouraging me whenever I faced any difficulties throughout my study
period. Lastly, I would like to thank all my classmates and friends for their
cooperation and camaraderie and also those who had supported me in any aspects
throughout the project. Once again sincerely thank you all for supporting me to
complete this project successfully.
v
ABSTRACT
Efficiency traffic management is important in order to manage the traffic
efficiently especially during unexpected situations. To do so, continuous and reliable
information on traffic situation is important to users so that they can plan their route
by avoiding traffic congestion. UAV images are an important means by which
detection and classification of vehicle can be carried out to assist the traffic
management. This is done by capturing the latest information of the road situation
and reporting it to road users. In this study, I develop a procedure to detect and count
the number of vehicles from three different height categories of UAV images.
Initially, two unsupervised classification methods, ISODATA and K-Mean, are
considered to separate vehicle and non-vehicle pixels from the UAV images. The
study were conducted in two phase; phase one is classifying the objects within the
image and phase two, determining the total number of vehicles within the image.
Firstly, the unsupervised classifications were implemented and analysed to cluster
similar pixels within the selected region. Secondly, region resizing is carried out to
reduce the portion which is non-relevant from an image. In order to get the total
amount of vehicles in an image, a standard amount of pixels for a single vehicle is
computed. The accuracy assessment in this study is measured by means of accuracy
and error percentage. This is done by comparing between the actual numbers of
vehicle with the number of vehicles computed from UAV images. For both
classifiers, low category images are found to have the highest accuracies. Further
study needs to focus on an automated technique of resizing the region to reduce the
non-relevant portion from an image in order to get the exact regions of vehicles.
vi
ABSTRAK
Pengurusan trafik yang cekap adalah penting bagi menguruskan lalu lintas dengan
cekap terutama semasa dalam keadaan yang tidak dijangka. Untuk berbuat demikian,
maklumat keadaan trafik yang berterusan dan boleh dipercayai adalah penting
kepada pengguna supaya mereka dapat merancang laluan mereka dengan mengelak
kesesakan lalu lintas. Imej UAV adalah penting yang mana pengesanan dan
pengkelasan kenderaan boleh dilakukan untuk membantu pihak pengurusan lalu
lintas. Ini dilakukan dengan menangkap maklumat terkini keadaan jalan dan
melaporkan kepada pengguna jalan raya. Dalam kajian ini, saya membangunkan
prosedur untuk mengesan dan mengira bilangan kenderaan dari tiga kategori
ketinggian yang berbeza imej UAV. Pada mulanya, dua kaedah klasifikasi tanpa
pengawasan, ISODATA dan K-Min, digunakan untuk memisahkan piksel kenderaan
dan bukan kenderaan dari imej UAV. Kajian ini telah dijalankan dalam dua fasa; fasa
pertama mengklasifikasikan objek dalam imej dan fasa kedua menentukan jumlah
bilangan kenderaan dalam gambar. Dalam fasa pertama, klasifikasi tanpa
pengawasan telah dilaksanakan dan dianalisis untuk kelompok piksel yang sama di
dalam rantau yang dipilih. Dalam fasa kedua, pensaizan rantau dijalankan semula
untuk mengurangkan bahagian yang tidak berkaitan daripada imej. Dalam usaha
untuk mendapatkan jumlah kenderaan dalam imej, jumlah piksel piawan bagi
kenderaan tunggal dihitung. Penilaian ketepatan dalam kajian ini diukur berdasarkan
peratusan ketepatan dan kesilapan. Ini dilakukan dengan membandingkan antara
jumlah sebenar kenderaan dengan bilangan kenderaan yang dihitung dari imej UAV.
Bagi kedua-dua pengkelas, imej kategori rendah didapati mempunyai ketepatan
tinggi. Kajian lanjut perlu memberi tumpuan kepada teknik pensaizan rantau secara
automatik bagi mengurangkan bahagian yang tidak berkaitan daripada imej untuk
mendapatkan kawasan sebenar kenderaan.
vii
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
1.7 Conclusion
6
viii
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction
7
2.2 Type of Vehicle
8
2.2.1 Statistic of Vehicle
2.3 Remote Sensing Interest
10
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
2.4 Unsupervised Learning
15
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
3.2.5 Phase Five – Conclusion
31
ix
3.3 Project Schedule and Milestone
32
3.4 Design on Vehicle Detection and Classification
33
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
3.4.8 Output
37
38
3.4.9 Accuracy Assessment / Testing,
Compare and Analysis
3.5 Conclusion
38
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
Vehicle Detection
53
x
4.9 Calculate the Number of Pixel for
Vehicle Detection
4.10 Output
55
55
4.11 Accuracy Assessment / Testing,
Compare and Analysis
4.12 Conclusion
62
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
REFERENCES
74
xi
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
6.2
Accuracy Assessment of Low Category
71
xii
LIST OF FIGURES
FIGURE
TITLE
2.1
Sales market of vehicle in 2012 to 2013
2.2
Sales and production report in December of 2013 and
PAGE
10
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
4.4.4
Classifier of ISODATA in Low category
43
xiii
4.4.5
Classifier of ISODATA in Medium category
43
4.4.6
Classifier of ISODATA in High category
44
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
5.3.3
Output of Sample 3
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.
Nowadays, people start to have concern on traffic situation at their living and
working areas. Traffic situation already is as a part of daily life of people. This is
because no matter where they want either to go to school study, working, playing or
anywhere, they still need vehicle to send them going to the right destination. Heavy
2
traffic situation, undeniably, will bring the negative effects to them such as those in
terms of emotional, punctually attending, driving attitude and others.
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.
UAV image is the source to be processing in this project. UAV also named as
unmanned aerial vehicle. It is a aircraft without human pilot. It is also act a remote
sensing to get more information or detail toward the object by detecting and classify
the objects on Earth. Therefore, it will through this aircraft to collect the source or
situation on the road. Therefore, i will use the source to done the processing of
detection and classification to identify the result.
3
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
Traffic information is nowadays being more concerned by a motorists using the
road to travel to different places. However, the amount of vehicle is increasing day
by day. Road traffic needs to be efficiently managed to prevent road accident,
serious traffic jam and others. Such situations can cause negative effects not only to
motorists but also others. The convensional approaches of managing traffic is by
police patrol and CCTV. Nevertheless, such approaches are logistically expensive
and not practical for large areas because the need to place a large number of police
patrol and CCTV in order to manage traffic. Therefore an improved way for traffic
management is critically needed especially to provide motorists with the latest traffic
condition and to help detecting stolen vehicles.
4
1.3. Objective
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
The data that will be used in this project is collect from Unmanned Aerial
Vehicle. It will collect the image from certain area. It also enables collect
movement image with support more clearly image to be done analysis.
Therefore, it can be done detection and segmentation directly and
effective. However, in this project, there are just focuses on certain type
vehicle such as car, lorry and bus. The reason there are just focuses on
this few type of vehicle is because this type of vehicle only always get the
more spacing on the road and affect the traffic situation. Besides that,
their size is bigger, this will cause the detection done in smooth and result
5
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
This project will be benefit toward government traffic management
particularly to determine the amount of vehicle using certain road. Therefore, it can
help toward reporting the traffic situation to the motorists. In this way the motorists
can have alternative travel planning in advance in order reach journey in a faster and
safer way. Besides that, government also can get to know the requiring of motorists
to make the adjustment to improve the situation such as enlarging the road size to
suit the capacity of vehicles using a particular road. In addition, this project can
decrease the number of stolen vehicles by providing information regarding the
location of the stolen vehicles in a shorter time. .
6
1.6. Expected Output
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
This chapter are just brief on this project idea and direction. I have provided
the rationale for carrying this project and highlighted the problems long faced by
motorists. I believe this project will have significant impact to the societies upon its
completion.
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.
Literature reviews from journal and research papers are more emphasize on
several part which are result and discussion. Through the result we can identify the
outcome from their research and for the discussion it help us to determine the
8
significance of project and problem for further future research. Therefore, from the
result and discussion of paper, it can carry out the abstract of that project. Trusted or
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:
For a normal rigid truck size dimension is:
VEHICLE (UAV)
ANG KUAN KEE
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
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
_______ TERHAD
(Mengandungi maklumat yang berdarjah
keselamatan atau kepentingan Malaysia
seperti yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
(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,
Taman Bayu Perdana, 41200 Klang
Selangor
Tarikh: ______________
DR. ASMALA BIN AHMAD
Nama Penyelia
Tarikh: ____________
CATATAN: * Tesis dimaksudkan sebagai Laporan Akhir Projek Sarjana Muda
(PSM)
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihakberkuasa
VEHICLE DETECTION AND COUNTING FROM UNMANNED AERIAL
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
2015
ii
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:_______
(DR. ASMALA BIN AHMAD)
iii
DEDICATION
I would like to dedicate my final year project report to my beloved family, friends
and my lecturers. For my supervisor, Dr. Asmala Ahmad and my evaluator, Dr
Sharifah Sakinah, always guiding, comment and helping me to complete up this
project. Besides that, family is the one always give me spirit support and life-long
love to me. They have been very supportive and encouraging in completion of my
thesis and throughout the years of my studies. Furthermore I also would like to
dedicate to the friends who have been supportive and helpings throughout the project
development.
iv
ACKNOWLEDGEMENT
I would like to take an opportunity to express my gratitude to those who contributed
and showed their kindness in helping and assisting me to complete this project. First
and foremost, I would like to thank my supervisor, Dr. Asmala Ahmad and my
evaluator, Dr. Sharifah Sakinah for all the guidance and encouragement. I am indebt
for the patient, support and motivation from the beginning to the end of my project.
Thank you also for instructing and teaching me in developing the understanding of
the project and eventually me enable to grasp the essence of this project. Besides that,
I also like to thank a PhD student, Mr. Nasruddin Abu Sari whom provided me with
the UAV data for this project. Most important, I am thankful to my family whom
continuously keeping up my spirit and supporting me in term of financial and moral.
They keep on encouraging me whenever I faced any difficulties throughout my study
period. Lastly, I would like to thank all my classmates and friends for their
cooperation and camaraderie and also those who had supported me in any aspects
throughout the project. Once again sincerely thank you all for supporting me to
complete this project successfully.
v
ABSTRACT
Efficiency traffic management is important in order to manage the traffic
efficiently especially during unexpected situations. To do so, continuous and reliable
information on traffic situation is important to users so that they can plan their route
by avoiding traffic congestion. UAV images are an important means by which
detection and classification of vehicle can be carried out to assist the traffic
management. This is done by capturing the latest information of the road situation
and reporting it to road users. In this study, I develop a procedure to detect and count
the number of vehicles from three different height categories of UAV images.
Initially, two unsupervised classification methods, ISODATA and K-Mean, are
considered to separate vehicle and non-vehicle pixels from the UAV images. The
study were conducted in two phase; phase one is classifying the objects within the
image and phase two, determining the total number of vehicles within the image.
Firstly, the unsupervised classifications were implemented and analysed to cluster
similar pixels within the selected region. Secondly, region resizing is carried out to
reduce the portion which is non-relevant from an image. In order to get the total
amount of vehicles in an image, a standard amount of pixels for a single vehicle is
computed. The accuracy assessment in this study is measured by means of accuracy
and error percentage. This is done by comparing between the actual numbers of
vehicle with the number of vehicles computed from UAV images. For both
classifiers, low category images are found to have the highest accuracies. Further
study needs to focus on an automated technique of resizing the region to reduce the
non-relevant portion from an image in order to get the exact regions of vehicles.
vi
ABSTRAK
Pengurusan trafik yang cekap adalah penting bagi menguruskan lalu lintas dengan
cekap terutama semasa dalam keadaan yang tidak dijangka. Untuk berbuat demikian,
maklumat keadaan trafik yang berterusan dan boleh dipercayai adalah penting
kepada pengguna supaya mereka dapat merancang laluan mereka dengan mengelak
kesesakan lalu lintas. Imej UAV adalah penting yang mana pengesanan dan
pengkelasan kenderaan boleh dilakukan untuk membantu pihak pengurusan lalu
lintas. Ini dilakukan dengan menangkap maklumat terkini keadaan jalan dan
melaporkan kepada pengguna jalan raya. Dalam kajian ini, saya membangunkan
prosedur untuk mengesan dan mengira bilangan kenderaan dari tiga kategori
ketinggian yang berbeza imej UAV. Pada mulanya, dua kaedah klasifikasi tanpa
pengawasan, ISODATA dan K-Min, digunakan untuk memisahkan piksel kenderaan
dan bukan kenderaan dari imej UAV. Kajian ini telah dijalankan dalam dua fasa; fasa
pertama mengklasifikasikan objek dalam imej dan fasa kedua menentukan jumlah
bilangan kenderaan dalam gambar. Dalam fasa pertama, klasifikasi tanpa
pengawasan telah dilaksanakan dan dianalisis untuk kelompok piksel yang sama di
dalam rantau yang dipilih. Dalam fasa kedua, pensaizan rantau dijalankan semula
untuk mengurangkan bahagian yang tidak berkaitan daripada imej. Dalam usaha
untuk mendapatkan jumlah kenderaan dalam imej, jumlah piksel piawan bagi
kenderaan tunggal dihitung. Penilaian ketepatan dalam kajian ini diukur berdasarkan
peratusan ketepatan dan kesilapan. Ini dilakukan dengan membandingkan antara
jumlah sebenar kenderaan dengan bilangan kenderaan yang dihitung dari imej UAV.
Bagi kedua-dua pengkelas, imej kategori rendah didapati mempunyai ketepatan
tinggi. Kajian lanjut perlu memberi tumpuan kepada teknik pensaizan rantau secara
automatik bagi mengurangkan bahagian yang tidak berkaitan daripada imej untuk
mendapatkan kawasan sebenar kenderaan.
vii
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
1.7 Conclusion
6
viii
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction
7
2.2 Type of Vehicle
8
2.2.1 Statistic of Vehicle
2.3 Remote Sensing Interest
10
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
2.4 Unsupervised Learning
15
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
3.2.5 Phase Five – Conclusion
31
ix
3.3 Project Schedule and Milestone
32
3.4 Design on Vehicle Detection and Classification
33
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
3.4.8 Output
37
38
3.4.9 Accuracy Assessment / Testing,
Compare and Analysis
3.5 Conclusion
38
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
Vehicle Detection
53
x
4.9 Calculate the Number of Pixel for
Vehicle Detection
4.10 Output
55
55
4.11 Accuracy Assessment / Testing,
Compare and Analysis
4.12 Conclusion
62
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
REFERENCES
74
xi
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
6.2
Accuracy Assessment of Low Category
71
xii
LIST OF FIGURES
FIGURE
TITLE
2.1
Sales market of vehicle in 2012 to 2013
2.2
Sales and production report in December of 2013 and
PAGE
10
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
4.4.4
Classifier of ISODATA in Low category
43
xiii
4.4.5
Classifier of ISODATA in Medium category
43
4.4.6
Classifier of ISODATA in High category
44
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
5.3.3
Output of Sample 3
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.
Nowadays, people start to have concern on traffic situation at their living and
working areas. Traffic situation already is as a part of daily life of people. This is
because no matter where they want either to go to school study, working, playing or
anywhere, they still need vehicle to send them going to the right destination. Heavy
2
traffic situation, undeniably, will bring the negative effects to them such as those in
terms of emotional, punctually attending, driving attitude and others.
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.
UAV image is the source to be processing in this project. UAV also named as
unmanned aerial vehicle. It is a aircraft without human pilot. It is also act a remote
sensing to get more information or detail toward the object by detecting and classify
the objects on Earth. Therefore, it will through this aircraft to collect the source or
situation on the road. Therefore, i will use the source to done the processing of
detection and classification to identify the result.
3
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
Traffic information is nowadays being more concerned by a motorists using the
road to travel to different places. However, the amount of vehicle is increasing day
by day. Road traffic needs to be efficiently managed to prevent road accident,
serious traffic jam and others. Such situations can cause negative effects not only to
motorists but also others. The convensional approaches of managing traffic is by
police patrol and CCTV. Nevertheless, such approaches are logistically expensive
and not practical for large areas because the need to place a large number of police
patrol and CCTV in order to manage traffic. Therefore an improved way for traffic
management is critically needed especially to provide motorists with the latest traffic
condition and to help detecting stolen vehicles.
4
1.3. Objective
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
The data that will be used in this project is collect from Unmanned Aerial
Vehicle. It will collect the image from certain area. It also enables collect
movement image with support more clearly image to be done analysis.
Therefore, it can be done detection and segmentation directly and
effective. However, in this project, there are just focuses on certain type
vehicle such as car, lorry and bus. The reason there are just focuses on
this few type of vehicle is because this type of vehicle only always get the
more spacing on the road and affect the traffic situation. Besides that,
their size is bigger, this will cause the detection done in smooth and result
5
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
This project will be benefit toward government traffic management
particularly to determine the amount of vehicle using certain road. Therefore, it can
help toward reporting the traffic situation to the motorists. In this way the motorists
can have alternative travel planning in advance in order reach journey in a faster and
safer way. Besides that, government also can get to know the requiring of motorists
to make the adjustment to improve the situation such as enlarging the road size to
suit the capacity of vehicles using a particular road. In addition, this project can
decrease the number of stolen vehicles by providing information regarding the
location of the stolen vehicles in a shorter time. .
6
1.6. Expected Output
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
This chapter are just brief on this project idea and direction. I have provided
the rationale for carrying this project and highlighted the problems long faced by
motorists. I believe this project will have significant impact to the societies upon its
completion.
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.
Literature reviews from journal and research papers are more emphasize on
several part which are result and discussion. Through the result we can identify the
outcome from their research and for the discussion it help us to determine the
8
significance of project and problem for further future research. Therefore, from the
result and discussion of paper, it can carry out the abstract of that project. Trusted or
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:
For a normal rigid truck size dimension is: