I:

MOTION DETECTION AND SIMULATION STUDY ON HUMAN WALKING BEHAVIOR

Cheong Wai Loon

Master of Engineering 2012

PUS31 Khidmat,MakJulBat Akaat:J11lk UNJVERSm MALAYSIA SARAWAf(

MOTION DETECTION AND SIMULATION STUDY ON HUMAN WALKING

BEHAVIOUR

CHEONG W AI LOON

A thesis submitted

in fulfillment ofrequirements for the degree of Master of Engineering

Faculty of Engineering

UNIVERSITI MALAYSIA SARA W AK

2012

,...

To my belovett family, fiancee and friends

•

ACKNOWLEDGEMENT

I would like to take this opportunity to express my thanks and appreciations to people

who support and encourage me in accomplish this project. This research was supported by

Universiti Malaysia Sarawak (UN'IMAS) under Fundamental Research Grant Scheme (FRGS)

through providing good facilities in conducting the research.

First of all, I would like to thank my project supervisor Dr. Syed Tarmizi Syed Shazali

and co-supervisor Mr. Shahrol Mohamaddan for their patient, supervision, guidance and

continuous support throughout the project. My wife, Lee Ruoh Cheng and my beloved

parents, without your support, encouragement and care my whole project would be

incomplete. Besides that I would like to thank my friends who have also supported me and

also helped me throughout the project. Thank you to all the parties involved.

ABSTRACT

~uman walking behaviour modelling is an important topic in different area of studies.

Architects, interior designer and transport engineers want to design and integrated a good

building and facilities with particular emphasis on safety issues for pedestrians especially

during evacuation scenario when emergency. The objective of this project is to analyse the

human walking behaviour from video data, and develop an algorithm for human walking

behaviour and emergency evacuation simulatioy The motion detection system were

developed using Video and Image Processing Blockset in MAT LAB to collect the centroid

coordinate of the pedestrians from the video to analyse the human walking behaviour. The

walking speed and acceleration of the pedestrian are calculated and implemented into the

simulation. The buildings in the simulation are designed in 3D World Studio. The simulation

is developed using DarkBasic Professional with the additional extension of Dark AI. There

are two categories of human in the simulation which are adult and elderly with different

walking speed. Two types of environments are developed in the simulation to study the

human walking behaviour which are empty space and building simulation. The analysis ofthe

simulation is the time travel for each entity to reach destination and total evacuation time in

building simulation. The bottleneck in the building design can be identified from the

observation ofthe building simulation.

11

ABSTRAK

Pemodelan kelakuan berjalan manusia adalah topik yang penting dalam kajian yang

berbeza. Arkitek, pereka dalaman dan jurutera pengangkutan perlu untuk mereka bentuk dan

integrasi bangunan dan kemudahan yang baik dengan memberi penekanan kepada isu-isu

keselamatan untuk pejalan kaki terutamanya semasa senario kecemasan. Objektif projek ini

adalah mengumpul dan menganalisis tingkah laku manusia berjalan kaki dari data video, dan

pembangunan algoritma bagi simulasi tingkah laku manusia yang berjalan dan pemindahan

kecemasan. Sistem pengesanan gerakan telah dibangunkan dengan menggunakan Video and

Image Processing Blockset dalam MATLAB untuk mengambil koordinat sentroid daripada

pejalan kaki dalam video untuk menganalisis tingkah laku manusia yang berjalan. Kelajuan

dan pecutan pejalan kaki telah diki.ra dan dilaksanakan ke dalam simulasi. Reka bentuk

bangunan dalam simulasi direka di 3D World Studio. Simulasi telah dibangunkan

menggunakan DarkBasic Profesional dengan sambungan tambahan Dark AI. Terdapat dua

kategori manusia dalam simulasi iaitu orang dewasa dan warga tua dengan kelajuan berjalan

yang berbeza. Dua jenis persekitaran telah dibangunkan dalam simulasi untuk mengkaji

tingkah laku berjalan manusia iaitu simulasi ruang kosong dan bangunan. Analisis dalam

simulasi adalah masa perjalanan untuk setiap entiti untuk sampai ke destinasi dan jumlah

masa pemindahan dalam simu1asi bangunan. Kesesakan dalam reka bentuk bangunan boleh

dikenal pasti dari pemerhatian simulasi bangunan.

III

Pusat Klaidmal Maklumat Akademi UNlVERSm MALAYSIA SAKAWA](

TABLE OF CONTENTS

ACKNOWLEDGEMENT

ABSTRACT

ABSTRAK

TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

LIST OF ABBREVIATIONS

Chapter 1 INTRODUCTION

1.1 Background of Study

1.2 Problem Statement

1.3 Objective

Chapter 2 LITERATURE REVIEW

2.1 Introduction

2.2 Image Processing

2.3 Motion Detection

2.4 Anthropometric

2.5 Field ofVision

2.6 Personal Space

2.7 Pedestrian Characteristics

2.7.1 Macroscopic Characteristics

2.7.2 Microscopic Characteristics

IV

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III

IV

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Xli

XIV

3

3

4

5

6

9

11

12

14 I 14

17

2.7 .3 Pedestrian Characteristics Research 19

2 .8 Simulation on Human Walking Behaviour 22

2.9 Summary 24

Chapter 3 METHODOLOGY

3.1 Introduction 25

3.2 Video Data Recording 26

3.2.1 Location Selection 27

3.2.2 Observation Duration 30

3.3 Data Collection (Image Processing) 31

3.4 Data Analysis 32

3.5 Simulation and Modelling Development 33

3.5.1 Empty Space Design 34

3.5.2 Building Design 34

3.6 Summary 35

Chapter 4 DATA COLLECTION AND ANALYSIS

4.l Introduction 36

4.2 Motion 39

4.3 Background Estimation 40

4.4 Segmentation 41

4.5 Detection 44

4.6 Data Analysis 46

4.6.1 Analysis of Location A 47

4.6.2 Analysis of Location B 51

4.7 Results of Real Life Data Collection 53

v

584.8 Discussion

594.9 Summary

Chapter 5 SIMULATIONS AND MODELLING DEVELOPMENT

605.1 Introduction

605.2 Simulation Rules

5.3 Building Designs 63

5.4 Simulation Design 65

5.4.1 Simulation on Empty Space 69

5.4.2 Simulation on Building 74

5.4.3 Help Display in Simulation 78

5.5 Ability ofArtificial Intelligence 82

5.6 Simulation Data Analysis 83

5.7 Summary 85

Chapter 6 RESULTS AND DISCUSSION

6.1 Introduction 86

6.2 Results ofEmpty Space Simulation 87

6.3 Results ofBuilding Simulation 92

6.4 Summary 102

Chapter 7 CONCLUSION AND RECOMMENDATIONS

1037.1 Introduction

1037.2 Conclusion

106

108

7.3 Recommendations

REFERENCES

115APPENDIX A

VI

LIST OF FIGURES

-

Figure Title Page

Figure 2.1 Image processing steps. 5

males (Goldsmith, 1976).

females (Goldsmith, 1976).

bubbles (Hall, 1966).

Estimation Subtraction Method.

Figure 4.2 Simulink Model to Track People.

Figure 2.2 Mean average (50th percentile) dimensions of adult 10

Figure 2.3 Mean average (50th percentile) dimensions of adult 11

Figure 2.4 Diagram of Edward T. Hall's personal reaction 13

Figure 3.1 Flow chart for project methodology. 26

Figure 3.2 Location A. 28

Figure 3.3 Set up location for Location A. 29

Figure 3.4 Location B. 29

Figure 3.5 Set up location for Location B. 30

Figure 3.6 Pedestrian selected space at Location A. 32

Figure 3.7 Pedestrian selected space at Location B. 33

Figure 4.1 Motion Detection Using Periodic Background 37

38

Figure 4.3 Generated Pulse used as Periodic Timer. 39

Figure 4.4 Background Estimation using median. 40

VII

41

42

Figure 4.5 Simulink Model with Background Estimation and

Pulse Generator.

Figure 4.6 (a) Input video image. (b) Background subtraction.

(c) Autothreshold. (d) Dilation and Erosion.

Figure 4.7 Simulink Model in Segmentation.

Figure 4.8 (a) Blobs drawn in the binary image (b) Blobs 44

drawn in the output image with blob counter.

Figure 4.9 Simulink Model in Detection.

Figure 4.10 Simulink Model in Display Result.

Figure 4.11 Analysis ofLocation A.

Figure 4.12 Find coordinate of pedestrian.

Figure 4.13 Analysis of Location B.

Figure 4.14 Trigonometric to calculate lp.

55

Figure 4.16 Graph plotted from Table 4.4.

Figure 4.15 Graph plotted from Table 4.1 .

43

45

46

47

48

51

52

58

Figure 4.17 (a) Background image at 8:47:53AM (b) Tracked 59

image with moving lorry (c) Background image

with static lorry as background at 8:48:20AM (d)

Tracked image with static lorry.

Figure 5.1 Simple Walking Simulation Test. 61

Figure 5.2 Time versus Dark AI speed. 62

Figure 5.3 Building Design I . 64

Figure 5.4 Building Design 2. 64

Figure 5.5 Building Design 3. 65

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-Figure 5.6 Flow Chart of Simulation. 66

Figure 5.7 Main menu of simulation. 66

Figure 5.8 3D adult human entities. 67

Figure 5.9 3D elderly human entities. 68

Figure 5.10 3D obstacles in the simulation. 68

Figure 5.11 Menu for Empty Space Simulation. 69

Figure 5.12 Simulation ofone-way empty space. 70

Figure 5.13 Flow Chart ofOne-way Empty Space Simulation. 71

Figure 5.14 Flow Chart ofTwo-ways Empty Space Simulation. 73

Figure 5.15 Simulation of two-way empty space. 74

Figure 5.16 Flow Chart of Building Simulation. 75

Figure 5.17 Menu for building simulation. 76

Figure 5.18 Building simulation. 77

Figure 5.19 Destination point in building simulation. 78

Figure 5.20 Help display in simulation. 79

Figure 5.21 Entity path to the destination in red line. 79

Figure 5.22 Obstacles boundary in blinking colour of green and 80

white.

Figure 5.23 View Arcs of entities. 81

Figure 5.24 Avoidance Angles ofentities in green. 81

Figure 5.25 Side view in simulation. 82

Figure 5.26 The shortest path blocked by boxes, AI entities 83

finds another path to the destination point.

IX

87 Figure 6.1 Average Speed versus Total Adult Entities walking

in one-way empty space.

Figure 6.2 Average Speed versus Total Adult Entities walking 88

in two-way empty space.

Figure 6.3 Comparison of adult walking in one-way and two­ 88

way graph.

Figure 6.4 Average Speed versus Percentage of Elderly in total 90

99 Entities walking in one-way empty space.

Figure 6.5 Average Speed versus Percentage of Elderly in 99 90

Entities walking in two-ways empty space.

Figure 6.6 Comparison of percentage of elderly walking m 91

one-way and two-way graph.

Figure 6.7 Time versus Number of Adult Entities evacuate 92

from Building Design 1.

Figure 6.8 Bottleneck area in Building Design 1. 93

Figure 6.9 Time versus Percentage Elderly m 99 Entities 94

evacuate from Building Design 1.

Figure 6.10 Time versus Number of Adult Entities evacuate 95

from Building Design 2.

Figure 6.11 Bottleneck exit in Building Design 2. 96

Figure 6.12 Time versus Percentage Elderly ill 99 Entities 97

evacuate from Building Design 2.

Figure 6.13 Time versus Number of Adult Entities evacuate 98

from Building Design 3.

x

Figure 6.14 Bottleneck exit in Building Design 3. 99

Figure 6.15 Time versus Percentage Elderly ill 99 Entities 100

evacuate from Building Design 3.

Figure 6.16 Comparison of total evacuation time in different 101

building design.

Figure 6.17 Comparison of total evacuation time versus 101

percentage of elderly in different building design

simulation.

Xl

,.....

LIST OF TABLES

Table Title

Table 2.1

Table 2.2

Table 3.1

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 5.1

Table A-I

Table A-2

Table A-3

Table A-4

Table A-5

Table A-6

Types of Spaces (Hall, 1966).

Comparison pedestrian characteristics of several

previous studies (Teknomo, 2002).

The schedule for videos recording.

The classification of pedestrians speed data.

Median, Average, Standard Deviation and Variance

of pedestrians speed in Location A and Location B.

Median, Average, Standard Deviation and Variance

of pedestrians' deceleration.

Classification of pedestrians' deceleration data.

Time to travel 800 pixels with Dark AI speed.

Pedestrians' speed at Location A.

Pedestrians' speed at Location B.

Pedestrians' deceleration from analysis of the

observation in video.

Results of adults walking one-way in empty space.

Results of adults walking two-ways in empty space.

Results of percentage elderly in total 99 entities

walking in one-way of empty space.

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21

31

54

56

57

57

62

116

118

119

120

121

122

123 Table A-7 Results of percentage elderly in total 99 entities

walking in two-way of empty space.

Table A-8 Results ofadults walking in Building Design 1. 124

walking in Building Design 1.

walking in Building Design 2.

walking in Building Design 3.

Table A-9 Results of percentage elderly in total 99 entities 125

Table A-I 0 Results of adults walking in Building Design 2. 126

Table A-II Results of percentage elderly in total 99 entities 127

Table A-12 Results of adults walking in Building Design 3. 128

Table A-13 Results of percentage eldedy in total 99 entities 129

Xlll

Abbreviations

a, b, Ip

AI

e TFI

E

E

et(t )

FCSIT

FE

FPS

f(x. y)

f(x. y. tJ

k

Ie;

L

1.0

Lc

Ld

La

Lp

LIST OF ABBREVIATIONS

Meaning

Pixel at coordinate i

Length [m]

Artificial intelligence

Central of Teaching Facilities I

Ratio ofenlargement for each pixel

Efficiency measure [m/ s]

Direction into which pedestrian i is driven at time t [s]

Faculty of Computer Science and Information Technology

Faculty of Engineering

Frame per second

Function of two variables

Function of two variables at time ti [s]

Pedestrian traffic density [ped/m2]

Traffic jam density [ped/ m2 ]

Length [m]

Length from start of the frame until the pedestrian trap [m]

Original length in centimeter [cm]

Length at d [pixel]

Real distance [m]

Total length [pixel]

XIV

-

!

LII

Lx

10

M

N

q

qi

R

r

RL

Tp

SAD

T

t

t/"

ttut

[J

u

~f

UNIMAS

V

VA

Vi

Length at u [pixel]

Enlargement of length of pedestrian walking at the coordinate

[m]

Travel distance of pedestrian in one second in centimeter [cm]

Area module [m2Iped]

Number of pedestrians observed [ped]

Flow rate [pedl minI m]

Deceleration of ilh pedestrian [rnIs2]

Ratio

Ratio of enlargement in length from up to down

Ratio oforiginal length

Ratio of enlargement at pixel yp

Sum ofabsolute difference

Observation time [s]

A verage travel time [s]

Time of pedestrian i'h to go in [s]

Time of pedestrian i'h to go out [s]

Uncomfortableness measure [ped- I]

Space mean speed [rnIs]

Free flow speed [rnImin]

Universiti Malaysia Sarawak

Time mean speed [ml s]

Average speed ofeach pedestrian [rnIs]

Instantaneous speed of the ith pedestrian [m/s]

xv

w

Xc

X;, 9!,~, Yt

y

Yp

Intended velocity of pedestrian i [m/s]

Velocity of pedestrian i at time t [m/s]

Width [m]

Width from down until coordinate of pedestrian [m]

Coordinate x

Coordinate x of the pedestrian in centimeter [cm]

Time average from t/ to t2 [s]

Coordinate x of the pedestrian in pixel

Coordinate y

Average coordinate y of a pedestrian

XVI

CHAPTER!

INTRODUCTION

1.1 Background of Study

Human walking behaviour modelling is an important topic in different area of studies.

Architects are interested to fmd out the optimal criteria for space design by understanding

how individuals move around buildings. Transport engineers need to integrate the

transportation facilities with particular emphasis on safety issues for pedestrians. The

evacuation scenario during emergency is specially emphasis to prevent injury or fatality.

Human behaviour is the potential and expressed capacity of the phases of human life

for physical, mental, and social activity. Human beings have a typical life course that consists

of successive phases of growth, each of which is characterized by a distinct set of physical,

physio logical, and behavioural features. These phases are prenatal life, infancy, childhood,

adolescence, and adulthood. Human behaviour influenced by emotion, environmental factors,

heredity and social (Space Perception, 20 10).

Human behaviour modelling consider the human as a device with a great number of

internal mental states, each with its own particular control behaviour and interstate transition

probabilities (Pentland & Liu, 1999). Human behaviour modeling is a very complex and

varies with different people. In the project, some of the factor are considered and used to

simplify the research. By using human behaviour modelling or human behaviour simulations,

the movement of the human can be predicted with different situation or environment. The

designer can design the layout of the building by knowing the walking flow of the human and

make sure human able to go to the destination in the shortest time.

The traffic flow characteristics could be divided into two categories which are

microscopic level and macroscopic level. Microscopic level invo lves individual pedestrian

characteristics such as individual speed and individual interaction. Macroscopic level

involved all pedestrian movements in pedestrian facilities aggregated into flow, average speed

and area module. Macroscopic pedestrian studies concern on space allocation for pedestrians

in the pedestrian facilities. While microscopic pedestrian studies on every pedestrian as an

indjvidual and the behaviour of pedestrian interaction is measured (Teknomo, 2002).

Simulation was using computer software to represent the dynamic responses of one

system by the behaviour ofanother system modeled after it. A simulation uses a mathematical

description, or model, of a rea) system in the form of a computer program composed of

equations that duplicate the functional relationships within the real system. The resulting

mathematical dynamics form an analog of the behaviou~ of the real system, with the results

presented in the form ofdata when the program is running (Computer Simulation, 20 10).

The objective of the studies is to evaluate the human walking behaviour and the

effects of a proposed facilities and building design before its implementation. The

implementation of a design without pedestrian studies might lead to a very costly trial and

error and waste of time. Trial and error could be done in the simulation analysis level to

2

check the bottleneck or the problem ofthe building affecting the human walking flow. If the

analysis could prove a good performance, the design of the facilities and building can be

implemented which can save cost and time of building design before the building is

constructed.

1.2 Problem Statement

Presented models of automated data collection using image processing for pedestrian

movement is particularly hard to collect (Antonini & Bierlaire, 2006). Automated data

collection system is required to collect the pedestrian walking data. The awareness of

environmental problems has increased and the need for physical fitness encouraged the

demand to analysis, simulating and enhances the design of pedestrian facilities and building to

improve the safety and performance of the building (Teknomo, 2002).

1.3 Objective

The objectives ofthis project are:

l. To analyse the human walking behaviour from video data.

2. To develop an algorithm source code for human walking behaviour detection using

MAT LAB software.

3. To develop an algorithm source code for human walking behaviour and emergency

evacuation simulation using DarkBasic Professional software.

3

CHAPTER 2

LITERA TURE REVIEW

2.1 Introduction

Human walking behaviour is different between types of people due to different

factors. Factors that affect the walking speeds of pedestrians are the personal characteristics of

pedestrians (age, gender, size, health, etc.), characteristics of the trip (walking purpose, route

familiarity, luggage, trip length), properties of the infrastructure (type, grade, attractiveness of

environment, shelter), and environmental characteristics (ambient, and weather conditions)

(Daamen & Hoogendoorn, 2003).

The objective of this project is to analyse the walking behaviour by using motion

detection in the video and simulate the walking behaviour of human being. The previous

researches on the subject are reviewed to find the rules and behaviour pattern of the human

walking. The gathered information from the video data and previous research help in the

design phase of the simulation work. The studies are to evaluate the effect of a proposed

policy ofbuilding and facilities before its implementation.

4

Pusat Khidmat MakiuDiat Akademlh UNIVERsm MALAYSIA SARAWA]{

1.1 Image Processing

Image processing is a processing of digital images using computer techniques such as

analyzing, enhancing, compressing and reconstructing of image captured through scanning or

digital photography (Image processing, 2009). Most image processing using techniques to

processing image which is distinct as two-dimensional function, f(x, y), where x and y are

spatial (plane) coordinates (Gonzalez & Woods, 2008).

The teps in the image processing were image acquisition by importing the image or

directly from digital photography, analysis and manipulation of the image by accomplished

using various specialized software application, and output of the image (Image processing,

2009). The image processing steps is shown in Figure 2.1.

Image sensing Image Object ~ ~ and acquisition segmentation recognition

Figure 2.1: Image processing steps.

Image sensing and acquisition is the ftrst step in image processing. It is captured using

a video camera and a digitizing system to store the image data for subsequent analysis. Image

captured from the subject of interest is obtained by dividing the area into a matrix of discrete

picture elements which is called pixel where each element has a value that is proportional to

the light intensity of that portion of the scene where each pixel is converted into its equivalent

digital value by an Analog to Digital Convertor (ADC) (Groover, 2008).

5