PERPUSTAKAAN UMP

LED STREET LI Jill 111111111111111111 )TIVE AUTO BEAM 0000071311

NURUL FADZLINA BINTI JAMIN

This thesis is submitted as partial fulfillment of the requirements for the award of

the Bachelor of Electrical Engineering (Electronics)

Faculty of Electrical & Electronics Engineering

Universiti Malaysia Pahang

JUNE 2012

ABSTRACT

As vehicle population increases, the number of road accidents also increases.

Vital problems in transportation such as mobility and safety of transportation

considered more, especially in metropolitans and highway. It is because of the

reflection or refraction of light from different directions also contributed to this

accident. Due from that, the driver could not identify the types of lamps in their

environment whether it is street lamp or car lamp from others. When other drivers

use the high beam from the opposite direction, will cause the view is blurred and

glare. Due to that, many road users can be stuck with unintentional accident. The

project of Light Emitter Diode (LED) Street Lamp Detection for Automotive Auto

Beam Detection will be developed as addition features of the system to detect LED

Street Lamp. Basically, this project is to differentiate between LED Street Lamp and

car lamp to ignore LED noise when it is combined each other. The main purpose of

this project is to enhance the automated high beam detection system using image

processing technique in MATLAB, to detect LED street lamp for automated high

beam detection and also to detect the vehicle-rear lamp within its proper distance for

automated high beam detection. In order to implement this project, it was conducted

with computer simulation (MATLAB) by using image processing method. There are

two ways to identify the image or visual by its color or shape of the objects. My

expected outcome is to develop the Graphical Using Interface (GUI) by taking the

night vision images.

A

ABSTRAK

Bilangan kemalangan jalan raya meningkat seiring dengan pertambahan

bilangan kenderaan. Masalah ml berlaku terutamanya di metropolitan dan lebuh raya.

Hal mi adalah kerana pantulan atau pembiasan cahaya dari arah yang berlainan yang

juga menyumbang kepada kemalangan mi. Oleh yang demikian, pemandu tidak

dapat mengenal pasti jenis lampu dalam persekitaran mereka sama ada lampu jalan

atau lampu kereta danipada orang lain. Apabila pemandu lain menggunakan lampu

tinggi dari arah bertentangan, akan menyebabkan pandangan menjadi kabur dan

silau. Oleh kerana itu, banyak pengguna jalan raya boleh terjebak dengan

kemalangan yang tidak disengajakan. Projek "LED Street Lamp Detection for

Automotive Auto Beam Detection" akan dicipta sebagai salah satu istem untuk

mengesan Lampu LED Street. Secara amnya, projek mi adalah untuk membezakan

antara Lampu LED dan lampu kereta untuk untuk menghilangkan gangguan apabila

ia digabungkan antara satu sama lain. Tujuan utama projek mi adalah untuk

meningkatkan sistem lampu kereta automatic menggunakan teknik pemprosesan imej

(image processing) menggunakan MATLAB, untuk mengesan lampu jalan LED

secara automatik dan juga untuk mengesan lampu belakang kenderaan dalam jarak

yang betul secara automatik. Dalam melaksanakan projek ini, ia menggunakan

MATLAB dengan menggunakan kaedah pemprosesan imej. Terdapat dua cam untuk

mengenal pasti imej atau visual oleh warna atau bentuk objek. Akhir sekali, project

mi adalah untk menghasilakan saW system (GUI) dengan mengambil gambar path

waktu malam.

VII

CONTENT

CHAPTER CONTENT PAGE

SUPERVISOR'S DECLARATION ii

STUDENT'S DECLARATION iii

DEDICATION iv

ACKNOWLEDGEMENT v

ABSTRACT vi

ABSTRAK vii

CONTENT viii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF SYMBOLS AND ABBREVIATIONS xvi

INTRODUCTION

1.0 Overview of Project 1

1.1 Problem Statement 2

1.2 Objectives of Project 4

1.3 Scope of the Project 4

1.4 Rationale and Significant 4

1.5 Summary 5

1.6 Project Outline 5

2 LITERATURE REVIEW

2.0 Introduction 7

2.1 Statistic of Road Accidents 7

2.2 Contributory Factors to Road Accidents 10

VIII

2.3 Car Lamp 10

2.4 Edge Detection 12

2.5 Segmentation 15

2.6 Clustering and Matching 18

2.7 Camera Configuration 20

2.7.1 Exposure Control 20

2.7.2 Color Configuration 22

2.8 Summary / Comments 22

3 METHODOLOGY

3.0 Introduction 24

3.1 Image Acquisition 27

3.2 Image Pre-processing 29

3.3 Feature Extraction 31

3.3.1 LED Street Lamp Image Processing 31

3.3.2 Vehicle-Rear Lamp Image Processing 33

3.4 Object Classification 35

3.4.1 Classification of LED Street Lamp 35

3.4.2 Classification of Vehicle-Rear Lamp 36

3.5 Decision Making 37

3.6 Graphical Using Interface for the System 39

4 RESULT AND DISCUSSIONS

4.1 Introduction 40

4.2 Result of High Beam 40

4.2.1 Case A: Without LED Street Lamp and

Vehicle-Rear Lamp Detection 41

4.2.2 Case B: LED Street Lamp Detection Only 42

4.3 Result of Low Beam 44

4.3.1 Case D: Vehicle-Rear Lamp and LED Street Lamp

Detection 44

4.4 Failed Image 46

4.5 Discussion of the System of Project 48

4.6 The Result Display on GUT 49

ix

CONCLUSION AND RECOMMENDATIONS

5.1 Introduction 50

5.2 Limitation 50

5.3 Recommendation 51

5.4 Conclusion 51

REFERENCES 52

A

LIST OF TABLES

TABLE TITLE PAGE

Table 1.1 The main road accidents statistic of Malaysia from

year 2000 to 2006. 3

Table 2.1 The number of accidents at every state in Malaysia. 9

Table 3.1 Result of decision making based on shape detection. 39

LIST OF FIGURES

FIGURE TITLE PAGE

Figure 1.1 Comparison on total cases of accident and fatality

on roadway from December 2008 to January 2009. 3

Figure 2.1 Normalized road accidents by population for Malaysia

year 2000 to 2006. 8

Figure 2.2 Normalized road accidents by vehicles for Malaysia

year 2000 to 2006. 8

Figure 2.3 Various Edge Detection Filters. 13

Figure 2.4 Vertical and Horizontal Edges. 14

Figure 2.5 Results of edge detection. Notice Canny had the

best results. 14

Figure 2.6 Grey scale difference between road sign, nuisance light,

tail lights and head lights. 16

Figure 2.7 Scale-normalized Laplacian operators for head lights. 17

Figure 2.8 Algorithm's overall overview for night-time Images. 17

Figure 2.9 Bright objects segmentation. 19

xii

Figure 2. 10 Vertical and horizontal projections. 19

Figure 2.11 Video frame captured with the automatic exposure control

enabled and Video frame captured using a lower static

exposure value. 21

Figure 3.1 General flow chart for overall system of Image Processing. 25

Figure 3.2 Flow chart for overall system of the Projec. t 25

Figure 3.3 Flow chart of the process. 26

Figure 3.4 Example of vehicle-rear lamp. 27

Figure 3.5 Example of combination LED street lamp and

vehicle-rear lamp. 28

Figure 3.6 Example of LED street lamp without vehicle. 28

Figure 3.7 Original RGB image. 29

Figure 3.8 Original image converted to level J4SV. 30

Figure 3.9 Original image converted to level gray-scale. 30

Figure 3.10 Only some area of image converted to RGB. 31

Figure 3.11 The spatial filtering process using Gaussian. 32

Figure 3.12 Binary image for LED street lamp process. 32

Figure 3.13 Remove the noise especially near to the LED street lamp. 33

Figure 3.14 Binary image for vehicle-rear lamp process. 34

XIII

xiv

Figure 3.15 The gap was filled to make it clear. 34

Figure 3.16 LED street lamp detection in yellow mark. 36

Figure 3.17 Vehicle-rear lamp detection in blue mark. 37

Figure 3.18 The result of LED street lamp and vehicle-rear lamp

Detection. 38

Figure 3.19 The result of the decision making for Low Beam. 38

Figure 320 The Graphical Using Interface (GUI) for the system 39

Figure 4.1 The result of without any detection of LED street lamp

and vehicle-rear lamp (Use High Beam). 41

Figure 4.2 High Beam for result of without LED street lamp and

vehicle-rear lamp detection display in Command Window. 42

Figure 4.3 The result of LED street detection (Use High Beam). 43

Figure 4.4 High Beam for result of LED street lamp detection

display in Command Window. 43

Figure 4.5 The result of LED street lamp and vehicle-rear lamp

detection (Use Low Beam). 45

Figure 4.6 Low Beam for result of LED street lamp and

vehicle-rear lamp detection display in Command Window. 45

Figure 4.7 The system was failed to detect the vehicle-rear lamp. 46

Figure 4.8 The system was failed to detect the LED street lamp and

vehicle-rear lamp. 47

xv

Figure 4.9 The system was failed to detect vehicle-rear lamp. 48

Figure 4.10 The interface of LED street lamp and vehicle-rear

lamp detection 49

LIST OF SYMBOLS AND ABBREVIATIONS

m - meter

% - percentage

- mean

ci - standard deviation

FKEE - Faculty of Electrical and Electronics

BEE - Bachelor of Electrical and Electronics

LED - Light Emitter Diode

GUI - Graphical User Interface

JKJR - Road Safety Department

DAS - Driver-Assisted-System

FYP - Final Year Project

2-1) - Two Dimensional

B & W - Black and White

ANSI - American National Standards Institute

HDR - High Dynamic Range

EV - Exposure Value

PAL - Phase Altering Line

CIE - International Commission on illumination

RGB - Red-Blue-Green

HSV - Hue-Saturation-Volume

PSM - Projek Sarjana Muda

MP - Mega Pixel

JPEG - Joint Photographic Experts Group

MA

CHAPTER 1

INTRODUCTION

1.0 Overview of Project

Vehicles such as transport cars, buses, trucks and motorcycles are among the

indispensable transportation in Malaysia to carry people and things from one destination

to another using the road as a medium of instruction. Therefore, the automatic vehicle

letection is a very important aspect in the road transportation system.

Although there is usually less traffic at night, it is common that accidents

Dccurred at night. Some of the reasons for night road accidents are that most drivers are

limited in night vision since some roads can be very dark with the absence of road

lamps. Hence, the driver will always use the high beam to help them have a better vision

luring driving on the road without realizing the behavior of the potential indirect hazard

for others. Hence, there is a need to have a system that can control the beam's mode to

help reduce driver's alertness to beam's mode changes.

Image analysis provides a number of effective techniques to enable it to detect

the target object in the image and it is widely used in traffic monitoring system.

1

2

Basically, the present invention detects and was also classifies objects in a real-time, in a

series of images obtained from cameras mounted on vehicles. The objects that will be

detected by image analysis techniques are vehicle headlights, taillights leading vehicle,

street lamps and also car rear lights for the car driving in front.

The auto beam control system, is developed by the classification of objects

created using image analysis is intended to alert the changes from high beam to low

beam or otherwise. Apart from that, the use of high beam is one of visual disturbances

and may create a safety hazard by making oncoming vision is blurred and glare.

It is a common thing in the way of road at night when the driver forgot to turn on

or off the beam when confronted with an incoming vehicle. This research will facilitate

in the automation of head lamp's change using a camera that detects the Street lamps,

incoming car lamps and rear lights.

1.1 Problem Statement

As shown in Table 1.1 and Figure 1. 1, it can be seen that the number of accidents

increase every year and these accidents include those that happened at night. The

darkness and the glare of vision is a major factor contributing to this accident. This is

because, the driver normally is confused between the road lights and car lights coming

from the opposite direction.

This will make the driver negligent and forgot to change the high beams to the

low beams. Indirectly, it leads to serious consequences to the driver coming from the Opp

osite direction and the cars in front that are in the same direction because the in

vision and will be glared. This is because, according to a study conducted by Marizuana

3

(2010), high beams is suitable for use by the driver if there is no incoming vehicle at a

distance of 10 yards to keep from disturbing the vision of other drivers [3].

Table 1.1: The main road accidents statistic of Malaysia from year 2000 to 2006 [1]

Year PopulationVehicle

Registered

Road

Accidents

Road

CasualtiesRoad Deaths

2000 23263600 10598804 250429 50200 6035

- 2001 23795300 11302545 265175 50473 5849

2002 24526500 12068144 279711 49552 5891

2003 25048300 12868934 298653 52741 6286

2004 25580000 13828889 326815 54091 6228

2005 26130000 14816407 328264 47012 6200

2006 26640000 15790732 341252 35425 6287

Perbandinqanlumlah Kes Kemaangan dan Kemalian Antara Uulan Disember 2008 - Januari 2009

2009

JANUAJU PERBEZAAN %

31,277 121 OA J 598 47 8.5

542 52 10.6

615 74 13.7

77 - 7,_ I 1,085

--179 - - 19.8

1,386 151 12.2 1

BULAN

2008

DISEMBER

JUHLAH KES KENALANGAN

31,156

JUN[AH KEMATIAN

551

JLJXLAH KENMANGAN iiAur

490

JUMLAH KEMALANGAN PARAH

541

JUMLAHCEDERAPARAH

687

JUNLAH KEMALANGAN RINGAN

906

JLJMI.AH CEBERA RINGAN

1,235

SUNBER: PORN

Analisis:JK]R

Figure 1.1: Comparison on total cases of accident and fatality on roadway from

December 2008 to January 2009 [2]

4

1.2 Objectives of Project

The main purpose of the project as listed:

(i) To enhance the automated high beam detection system using image processing

technique in MATLAB.

(ii) To detect LED street lamp for automated high low beam detection.

(iii)To detect the vehicle-rear lamp for automated high low beam detection.

1.3 Scope of the Project

In order to achieve the objectives, the project is focus on image processing of the

LED street lamp and detect image of vehicle rear-lamp of high low beam for the

algorithm development. On the other hand, for the vehicle rear-lamp, a few assumptions

are made, such that speed of the car in front is the same with the speed of car at the back.

The systems will only functions at night and the project will be simulated and analyzed

using MATLAB.

1.4 Rationale and Significant

The impact of this project is to help the driver to have a good technique of

driving on the road environment. The project is intended to improve the efficiency and is

a part of Driver-Assisted-System (DAS) so that the driver can drive their vehicle with

more effective control.

5

The significant of this research is to study the possibility detection of LED street

lamp and car rear lights for the car driving in front in order to make the high and low

beam changing automatically for solvents road accidents especially in highway and

hence give an alternative how to reduce or decrease the number of accidents in

Malaysia. Besides that, this study will help to establish a suitable parameter (7 m to 10

m) to overcome the beam detection for the car rear lights for the car driving in front.

1.5 Summary

This section or chapter is all about an overall project and explains the objectives

as well as the scope of the project in order to give an insight and idea of the project. On

the other hand, this section also comes out with the problem statement and the

significant of the system. On the next chapter will discuss about the literature review on

the functions, principles and application on each component of this project.

1.6 Project Outline

This thesis comprise 5 chapters altogether. Chapter 1 is on introduction, Chapter

2 is literature review, Chapter 3 contains methodology, Chapter 4 is on result and

discussion followed by last chapter which is Chapter 5 is conclusion.

6

Chapter 1 is the section where the introduction of the thesis of project will be

discussed. The overview of the project will be outlined including problem statement, the

projects' objective and also the project scope.

Chapter 2 there will be discussing the literature review of previous works from

thesis, journals and experiments that related to the project. The review includes LED

street lamp, vehicle-rear lamp and the methods that they used to analyze the image.

Chapter 3 represents the research methodology of this project. The step by step

procedure use to run this project from the beginning till end will be explained in detail.

This will also include the procedures or flow chart and processes involved for the

software development of the entire project.

Chapter 4 was consists of the experimental results and its respective analysis.

The result obtained will be discussed and explained here with the aid of figures. The

comparison of the every case will be discussed here.

In Chapter 5, here would be the summarized of the thesis and further

recommendation for the research.

CHAPTER 2

LITERATURE REVIEW

2.0 Introduction

Several related researches have been done to develop the system in order to

detect street lamp and vehicle-rear lamp of this project. The references for this project

are taken from journals, conferences and also articles that was cited directly from

reliable internet sources regarding the lamps detection.

2.1 Statistic of Road Accidents

As shown in the graph of Figure 2. 1, it was stated the normalized road accidents,

accident casualties and deaths by population for Malaysia in six years from 2000 to

2006.

7

8

Normalted Road Accidents. Accident Casualties and Deaths by Population for Malaysia 2000-2006

30.0

25.0

1—s—Road *cc1dø.s per 1000 pep.

1 -u-Road Casualles per 1000 I pop. 1-. --Road Deaths per 100000

pep.

2001 2002 2003 2004 2005

Figure 2.1: Normalized road accidents by population for Malaysia year 2000 to 2006 [1]

Figure 2.2: Normalized road accidents by vehicles for Malaysia year 2000 to 2006 [1]

In 2007 there were 247,780 victims of all seventies were 4% lower than in 2006.

The comparison of road accidents and death between 2000 until 2006 can be see based

on graph in Figure 2.2. In addition, 2,946 people were killed contributed to 7% lower

15.0

100

5.0

0.0 2

r-23.7-. 23.6

12.8

I 1.33

9

than in 2006. 27,774 people were seriously injured in 2000 which decreased to 3% from

2006 and 217,060 injured persons were below the 4% compared to 2006. Accordingly,

the government published a road safety strategy that is called More Secured Tomorrow

than Today [4]. This strategy is made is to achieve:

(i) 40% reduction in the number of people killed or seriously injured.

(ii) 50% reduction in the number of children killed or seriously injured.

(iii)10% reduction in bit rate for the number of injured victims.

Jabar Sadiq (2011) said, due to the increased number of deaths that occurred

during the holiday festival, operation of 'Ops Sikap 24' was carried out to overcome this

problem. 16.6% of fatal accidents there in the 259 and 289 people dying in them. In

Addition, the average time most of the accidents which occurred between 2.00pm to

6.00pm and 8.00pm to 10.00pm. A police study has shown that 85% of accidents are

caused by behavior, negligence and casual attitude while passing drivers. All the

information about the number of accidents at every state will show in Table 2.1 below

[6].

Table 2.1: The number of accidents at every state in Malaysia [6]

States Number of Accidents States Number of Accidents

Selangor 4380 Pahang 806

Johor 2346 Kelantan 640

Kuala Lumpur 1956 Melaka 638

Pulau Pinang 1392 Sabah 543

Perak 1377 Terengganu 536

Kedah 833 Sarawak 482

Negeri Sembilan 820 Perlis 68

10

2.2 Contributory Factors to Road Accidents

The failure of drivers to see properly is the most frequent contributing factor was

reported in 35% of all accidents. It is because; the driver is difficult to see clearly the

environmental conditions of road. This includes lighting, glare, rain and foggy weather.

In addition, four of the five factors most frequently reported are caused by the drivers'

carelessness in driving on the road and lost control of vehicle driven. These factors

resulted in 33% of fatal accidents [4].

On the other hand, S.0 Donald Lim (2002) has a different opinion in New Straits

Times, where road accidents are caused by driver's behavior that fails to give proper

signal when turning. Some other bad behaviors are when driver are the use of mobile

phone while driving and smoking while driving. These are also a factor that contributed

to the accident [5].

Meanwhile, H.M Heeza (2011) appears and claimed in Bernama Newspaper to

support the opinion expressed by the New Straits Times claimed; the negligent driver

and bad road conditions are the main cause of the increasing number of road accidents in

the country. Director of Kuala Lumpur Road Safety Department (JKJR) stressed that

70% of road accidents are caused by human negligence, 20% due to road conditions and

10% for vehicle problems [7].

2.3 Car Lamp

Marjzuana (2010) claimed in her Final Year Project (FYP) thesis, basically the

car lights are a member who holds a number of other lights in the shape of the loop.

11

Members who hold the light and hold the hole in the vicinity of each hole holding the

lamp. Some passages of light to communicate with every corner of the hold. Light

sources will be protected behind those passages of light, respectively. Each of the

members of the ring-shaped light thereon equidistantly spaced bumps condensed light,

and installed in one corner of their holdings to the parent. Therefore, the light source

changes are allowed, and the light in the shape of the closed-loop will run at all members

of the ring-shaped light when powered light source [3].

Moll and Gregory R (2004) claimed, the vehicle lighting systems consisting of

several parts of the mounting base has an elongated body and the body has a central

portion with a central bearing surface to contact the mounting surface. Parts of the body

including a pair of flanges having upwardly depending portion extends from the center

and downwardly depending on the parts that form a bearing surface spaced from the

central bearing surface for contacting the mounting surface. The center has opposed

longitudinal grooves of said bearing surface and the center of the longitudinal grooves

extending along said base. Longitudinal grooves and the inner surface of said flange and

defining the former elongated flexible strip light and kept in position by the flange of the

container. Significant band of light contained in the container, where bands of light

consist of a transparent polymer material body and the diversity of light sources

contained within the polymer body. In addition, the band of light with electrical contacts

for coupling light into the power supply source [8].

Moll and Gregory R (2004) also said, the vehicle lighting systems consisting of a

base having a longitudinal body, which has a longitudinal groove and against the distal

end. Each groove against the distal end with a pair of recessed mounting axis extending

into the distal end and extending generally parallel to the longitudinal grooves. Flexible

strip light beam position and stored by the band of light that is found in the longitudinal

grooves. At least one end cap coupled to the base of the distal end. End cap has a

shoulder abutting the distal end of the distal send of the base shoulder and projections,

including projections of axially extending up to the mounting recess for coupling to the

distal end of the base [8].

12

2.4 Edge Detection

Claypoole R, Lewis J, Bhashyarn, and Kelly (2009) entitlement, the most

effective way to do edge detection is divided into two categories; there are the gradient

and laplacian. Where the gradient method is detect edges by find the first derivative of

maximum and minimum image. Whereas, the laplacian method is to do a detection of

edges by find zero crossing in the second derivative image. The Figure 2.3 shows the

edge detected image using the gradient method (Roberts, Prewit, Sobel) and laplacian

method (Marrs-Hildreth) [9].

Claypoole R, Lewis J, Bhashyam, and Kelly (2009) also said, the Marr-Hildreth

not only has a lot more noise than the other methods, the low-pass filtering it uses

distorts the actual position of the facial features. Due to the nature of the Sobel and

Prewitt filters we can select out only vertical and horizontal edges of the image as shown

on Figure 2.4 below. This is very useful since we do not want to morph a vertical edge

in the initial image to a horizontal edge in the final image. This would cause a lot of

warping in the transition image and thus a bad morph [9].

On the other side, Jason Nearing, Rob Pickel and Anil Maliyekkel (2009) were

give their explanation' about the first step to segmenting images into regions is to

determine where the edges are. The several different edge-detection filters including true

sobel, true prewitt, Laplacian of Gaussian, and a mix of sobel and prewitt had been tried.

Due to that, one of the sources which are combination of sobel and prewitt could provide

a more optimal detection scheme. This 3x3 did in fact seem to work better than either

one of them alone. Another attempt was to use a Laplacian edge detector, but it picked

up so much noise that it was nearly impossible to determine any regions. Also, the

Laplacian filter did give thin edges, yet some were so thin that when tried filling in the

enclosed regions, many of the regions ended up bleeding together [10].