THE DEVELOPMENT OF A TRACKING

ALGORITHM FOR AMBULANCE DETECTION

USING SQUARING OF RGB AND HSV COLOR

PROCESSING TECHNIQUES

MOHAMMAD SYAWALUDIN SYAFIQ

BIN HASSAN

MASTER OF ENGINEERING (ELECTRONIC)

UNIVERSITI MALAYSIA PAHANG

UNIVERSITI MALAYSIA PAHANG

DECLARATION OF THESIS AND COPYRIGHT

Author’s Full Name : Mohammad Syawaludin Syafiq Bin Hassan

Date of Birth : 19 May 1990

Title : The Development of a Tracking Algorithm for

Ambulance Detection using Squaring of RGB and

HSV Color Processing Techniques

Academic Session : Semester II 2015/2016

I declare that this thesis is classified as:

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Official Secret Act 1972)

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organization where research was done)

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Certified By:

(Student’s Signature) (Supervisor’s Signature)

DR. MOHD RAZALI

900519-11-5167 BIN DAUD

New IC / Passport Number Name of Supervisor

Date : 22 August 2016 Date : 22 August 2016

√

SUPERVISORS’ DECLARATION

We hereby declare that we have checked this thesis and in our opinion, this thesis is

adequate in terms of scope and quality for the award of the degree of Master of

Engineering (Electronic Engineering).

Signature :

Name of Supervisor : Dr. Mohd Razali Bin Daud

Position : Senior Lecturer

Date : 22 August 2016

Signature :

Name of Co-supervisor : Dr. Kamarul Hawari Bin Ghazali

Position : Associate Professor

Date : 22 August 2016

Signature :

Name of Co-supervisor : Nurul Wahidah Binti Arshad

Position : Lecturer

Date : 22 August 2016

STUDENT’S DECLARATION

I hereby declare that the work in this thesis is my own except for quotations and

summaries which have been duly acknowledged. The thesis has not been accepted for

any degree and is not concurrently submitted for award of other degree.

Signature :

Name : Mohammad Syawaludin Syafiq Bin Hassan

ID Number : MEL12006

Date : 22 August 2016

THE DEVELOPMENT OF A TRACKING ALGORITHM FOR AMBULANCE

DETECTION USING SQUARING OF RGB AND HSV COLOR PROCESSING

TECHNIQUES

MOHAMMAD SYAWALUDIN SYAFIQ BIN HASSAN

Thesis submitted in fulfillment of the requirements

for the award of the degree of

Master of Engineering (Electronic)

Faculty of Electrical & Electronics Engineering

UNIVERSITI MALAYSIA PAHANG

AUGUST 2016

vi

TABLE OF CONTENTS

Page

DECLARATION

TITLE PAGE i

DEDICATION ii

ACKNOWLEDGEMENTS iii iii

ABSTRACT iiv

ABSTRAK v

TABLE OF CONTENTS vi

LIST OF TABLES ixx

LIST OF FIGURES x

LIST OF SYMBOLS xivv

LIST OF ABBREVIATIONS xv

INTRODUCTION 1

1.1 Background of Study 1

1.2 Problem Statement 3

1.3 Motivation 4

1.4 Research Objectives 5

1.5 Scope of Research 5

1.6 Contribution 6

1.7 Thesis Organization 6

LITERATURE REVIEW 7

2.1 Introduction 7

2.2 Technology of Emergency Response 7

2.3 Ambulance Detection Algorithm 12

2.4 Traffic Monitoring Using CCTV and Image Processing 15

2.5 Summary 19

vii

RESEARCH METHODOLOGY 21

3.1 Introduction 21

3.2 Tracking Algorithm 22

3.3 Specific Method and Step in Tracking Algorithm 28

3.3.1 Data Acquisition 29 3.3.2 Data Collection 30 3.3.3 Convolution Image Blurring 36 3.3.4 Color Processing in Tracking Algorithm 40

3.3.5 Image Enhancement 48 3.3.6 Image Arithmetic Operations 51 3.3.7 Final Stage of Image Processing in Tracking Algorithm 56

3.3.8 Feature Extraction 58 3.3.9 Classification 60

3.4 Summary 62

RESULTS AND DISCUSSIONS 63

4.1 Introduction 63

4.2 Output from Tracking Algorithm 64

4.3 Analysis Proposed Tracking Algorithm 65

4.3.1 Analysis in Data Acquisition Part 66 4.3.2 Performance Evaluation of Pre-processing 72

4.3.3 Performance Evaluation of Major Part in Tracking Algorithm 77 4.3.4 Performance Evaluation of Feature Extraction and Classification

85

4.4 Summary 89

CONCLUSIONS AND RECOMMENDATIONS 91

5.1 Summary of Contributions 91

5.2 Limitations 93

5.3 Future Research Directions 94

REFERENCES 95

APPENDIX 99

A List of Publications 99

B Important Coding in the Tracking Algorithm 100

viii

C Pre-processing Analysis 103

D HSV Color Processing Analysis 111

E RGB Color Processing Analysis 121

F Region Boundaries Analysis 129

G Different Cases for Tracking Algorithm Analysis 132

H Structure Element (SE) Analysis 150

I Data Collection Process 159

ix

LIST OF TABLES

Table No. Title Page

1.1 Statistics new registered motor vehicles by type and state,

Malaysia, 2013

2

3.1 Important specification of Sony Handycam DCR-SR47 30

3.2 Four rules for image subtraction operation 53

3.3 Four rules for image multiplication operation 56

3.4 Sum of threshold pixel value of image 61

4.1 Difference between 45o degree and 30o degree of images 66

4.2 Analysis of result from the position of CCTV 70

4.3 Analysis of overall performance detection for tracking

algorithm and other study

84

4.4 Comparison between three color processing for tracking

algorithm of ambulance detection

85

4.5 Analysis of overall performance detection for tracking

algorithm 87

4.6 Analysis time taken for an ambulance to reach at the traffic

light for different distance and speed 89

x

LIST OF FIGURES

Figure No. Title Page

1.1 Statistics on total road accidents and motor vehicles involved,

Malaysia 2004-2013

2

2.1 View of the Intelligent Transportation System. Adapted from

“Road reservation for fast and safe emergency vehicle

response using ubiquitous sensor network” by (Yoo et al.

2010

8

2.2 The movement when emergency cases occur 10

2.3 View of proposed algorithm. Adapted from “Intelligent

ambulance detection system” by (Andronicus & Maheswaran

2015)

13

2.4 Ambulance with red & blue colored siren in prototype.

Adapted from “Smart control of traffic signal system using

image processing” by (Parthasarathi et al. 2015)

14

2.5 Overall process of vehicle detection system. Adapted from

“Vehicle detection using morphological image processing

technique” by (Ajmal & Hussain 2010)

16

2.6 Image from the simulation, (a) grayscale reference image, (b)

grayscale current image, (c) mask and (d) mask applied on

current image

16

2.7 Overall process Tomato Maturity Estimator. Adapted from

“Color grading in tomato maturity estimator using image

processing technique” by (Syahrir et al. 2009)

18

3.1 Flow chart of the tracking algorithm 22

3.2 MATLAB code for the process of converting video to frames

of image

24

3.3 MATLAB code for the pre-processing stage 24

3.4 MATLAB code for the HSV color processing 25

3.5 MATLAB code for the RGB color processing 26

3.6 MATLAB code for the HSV color processing 26

3.7 MATLAB code for the multiplication of two color processing 27

3.8 MATLAB code for the dilation technique 27

xi

3.9 Implementation of the SE for dilation technique 28

3.10 MATLAB code for the classification stage 28

3.11 UMP ambulance used for data collection 29

3.12 The view of data collection area, (a) the intersection of the

road and (b) the pedestrian bridge

31

3.13 View of data acquisition plan A, on the pedestrian bridge, at

the right side of the road

32

3.14 View of data acquisition plan B, on the pedestrian bridge, at

the left side of the road

33

3.15 View of data acquisition plan C, at the center of the road,

before the traffic light

34

3.16 View of data taken from Plan A 35

3.17 View of data taken from Plan B 35

3.18 View of data taken from Plan C 36

3.19 Example of applying the box linear filter: (a) original image

and (b) output image corresponding to box mask

38

3.20 Example of applying the Gaussian low pass filter: (a) original

image and (b) Gaussian LPF result

39

3.21 Example mask (a) Prewitt and (b) Sobel 39

3.22 Example of applying the Prewitt mask: (a) original image and

(b) Prewitt filter result

40

3.23 Example of applying the Sobel mask: (a) original image and

(b) Sobel filter result

40

3.24 Flow chart of major part in tracking algorithm 42

3.25 Combination of two color space 43

3.26 Schematic showing how pixels of an RGB color image are

formed from the corresponding pixels of three components

45

3.27 RGB color model 45

3.28 Hue-Saturation-Value-Hex cone 47

3.29 HSV color processing (a) filtering image, (b) HSV color

space image

48

xii

3.30 Sample of SE 49

3.31 Basic operation of dilation and erosion, (a) binary image A,

(b) structuring element B, (c) dilation A and (d) erosion A.

51

3.32 Image subtraction operations 53

3.33 Image addition operations 54

3.34 Image multiplication operations 55

3.35 Image Pre-processing and Image Processing (a) filtering

image, (b) HSV color space, (c) image segmentation and (d)

contour tracing image

57

3.36 Image Processing (a) original image (b) segmentation and

binarization RGB color space

58

3.37 Flowchart of the algorithm shown feature extraction part 59

3.38 Feature extraction, (a) original image (b) desired object in

final image

60

4.1 Overall detection using tracking algorithm (a) red component

from cylindrical-coordinate of primary color space, (b) red

component from additive color space, (c) image squaring of

red components from two color space, and (d) detected

emergency vehicle

65

4.2 Analysis the angle of video camera 67

4.3 Position of CCTV, (a) at the right side of the road, (b) at the

middle side of the road and (c) on the left side of the road

68

4.4 The position of CCTV in the real time at the intersection of

the road

71

4.5 Original Image 72

4.6 Different region of interest ambulance at the end of the stage

detection by using: (a) box linear filter, (b) Gaussian LPF, (c)

Prewitt filter and (d) Sobel horizontal edge-emphasizing filter

73

4.7 Original image for an ambulance far from Handycam 74

4.8 Different region of interest ambulance at the end of the stage

detection by using: (a) box linear filter, (b) Gaussian LPF, (c)

Prewitt filter and (d) Sobel horizontal edge-emphasizing filter

75

4.9 Detection percentage of ambulance using different filter for

convolution image blurring stage

76

xiii

4.10 Detection percentage of red/orange vehicles using different

filter for convolution image blurring stage.

77

4.11 RGB color line (a) original image (b) segmentation and

binarization additive color

78

4.12 HSV color line (a) original image, (b) segmentation and

binarization after cylindrical coordinate color

79

4.13 Image processing for feature extraction (a) red component

from HSV analysis, and (b) red component from RGB

analysis

80

4.14 View of ambulance detected using multiplication of two color

spaces

81

4.15 View of ambulance detected using multiplication of two color

spaces

81

4.16 View of no detection for red vehicles using multiplication of

two color spaces

82

4.17 View of no detection for red vehicles using multiplication of

two color spaces

82

4.18 View of ambulance not detected 83

4.19 Classification of ambulance detection 86

4.20 Proposed system block diagram 88

4.21 Sketched diagram for the proposed system. 88

xiv

LIST OF SYMBOLS

Pmax new pixel level

Pin input pixel level

fmin minimum value of desired range

fmax maximum value of desired range

Pnew,RGB new pixel color value

Pin,RGB input pixel color value

minRGB lowest pixel value

maxRGB highest pixel value

Pj(rj) histogram with intensity levels of image

n total number of pixels

nj total number of gray level pixels

rj gray level

A original binary image

B symmetric structuring element

A1 first input image

C constant

k clusters

xv

LIST OF ABBREVIATIONS

RGB Red, Green, Blue

YCbCr Luminance; Chroma:Blue; Chroma:Red

HSV Hue, Saturation, Value

HIS Hue, Intensity, Saturation

HSL Hue, Saturation, Lumination

CCTV Camera Closed-circuit Television

CMYK Cyan, magenta, yellow, key (black)

Eq. equation

et al. and others

i.e. that is

EMAS Expressway Monitoring and Advisory System

CHART Coordinated Highway Action Response Team

ADRS Accident Detection and Reporting System

ESP Emergency Service Provider

APID All Purpose Incident Detection

DES Double Exponential Smoothing

ATMS Advanced Traffic Management Systems

ARRS Traffic Accident Recording and Reporting System

MOG Mixture of Gaussian Model

GPRS General Packet Radio Service

GSM Global System for Mobile Communications

GPS Global Positioning System

RFID Radio Frequency Identification