Object detecton and tracking

OBJECT DETECTION&

TRACKING

A mini project report submitted in partial fulfillment of the requirement for the award of the Degree of Batch

By V.S.Harsha Chowdary M.Subramanyam (083E1A0453) (083E1A0419) T.RamLaxman M.Prabakar (083E1A0404) (083E1A0422) J.Avanthi (083E1A0449)

Under the guidance of K.Sravani Kumari

Assistant professor.

Department of Electronics and Communication EngineeringJAGAN’S COLLEGE OF ENGINEERING AND

TECHNOLOGY::NELLORE(Affiliated to JNTU, Anantapur)

2011-12

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OBJECT TRACKING &

DETECTION A mini project report submitted in partial fulfillment of the requirement

for the award of the Degree of B.Tech

By

V.S.Harsha Chowdary M.Subramanyam (083E1A0453) (083E1A0419) T.RamLaxmaiah M.Prabakar (083E1A0404) (083E1A0422) J.Avanthi (083E1A0449)

Under the guidance of K.Sravani Kumari

Assistant professor.



2011-12

II



a) CERTIFICATE

This is to certify that the project report entitled ‘OBJECT DETECTION &

TRACKING’ being submitted by Mr.V.S.Harsha Chowdary in partial fulfillment for

the award of the Degree of Bachelor of technology in Electronics and

Communication Engineering to the Jawaharlal Nehru Technological University

Anantapur , is a record of bonafide work carried out by him under my guidance and

supervision.

Signature of Project supervisor Head of Department

Internal External

III

DECLARATION

I hereby declare that the mini project/ dissertation entitled, ‘OBJECT

DETECTION & TRACKING’ completed and written by me/ us has not been

previously formed the basis for the award of any degree or diploma or certificate.

Place: Name

Date: (Under signed)

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ACKNOWLEDGEMENT

It is a pleasure for us to add heartfelt words for the people who were the part of this

project in numerous ways who gave us the right support from the stage the idea was

conceived.

We are thankful to our honorable principal Prof.T.Krishnaiah,Ph.D for encouraging

us to do this project. We express our deep gratitude to all teaching and non teaching

staff members of college for help throughout provoking discussions, valuable

suggestions extended to us with immense care, willing and cooperation throughout

our work.

We are very much thankful to Prof.Ms.SV.Padmajarani,Ph.D Head of the

Department, Electronics and Communication Engineering. It is a pleasure to

acknowledge the research support of Venemsol Company .We are indebted to our

external guide Vinayagam Project Engineer for his undertaking and co-ordination

in this project through all its phases. We would like to express our gratitude to

other staff members for their valuable guidance and highly interactive attitude

without which the completion of the project would have been a difficult task.

It is great opportunity to render our sincere thanks to our internal guide

MS.K.Sravani Kumari,(M.tech) for his continued and valuable support during the

project. Our apologies for any oversights or shortcomings in the details provided in

this report. Last but not least we thank our family members and friends for being a

constant source of encouragement throughout this period.

V.S.Harsha Chowdary [email protected]

T.RamaLaxmaiah [email protected]

J.Avanthi [email protected]

M.Subramanyam [email protected]

M.Prabakar [email protected]

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mailto:[email protected]





ABSTRACT

Object detection & tracking in general, is a challenging problem. Easily we can

detect the object but difficulties in tracking objects can arise due to abrupt object

motion, changing appearance patterns of both the object and the scene, non rigid

object structures, object-to-object and object-to-scene occlusions, and camera

motion.

Tracking is usually performed in the context of higher-level applications that

require the location and/or shape of the object in every frame. A tracker assigns

consistent labels to the tracked objects in objects in different frames of a video.

This project is to detect, track and recognize objects from the video through

Matlab simulation.

The proposed system is able to distinguish transitory and stopped foreground

objects from static background objects in dynamic scenes; detect and distinguish

left and removed objects; classify detected objects into different groups such as

human, human group and vehicle; track objects and generate trajectory

information even in multi-occlusion cases

We propose a fast and robust approach to the detection and tracking of moving

objects by using blob algorithm. Objects are detected based on motion,using

background subtraction method. Tracking is then done using the Morphological

filter approach. So, we can avoid maximum noise in the video. Object detection

and tracking has several important applications such as security and surveillance.

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CONTENTS

BONAFIDE CERTIFICATE………………………...………………… ……………….III

DECLARATION BY THE CANDIDATE……………………………………………...IV

ACKNOWLEDGEMENT……………………………………………………………… V

ABSTRACT……………………………………………………………………………. VI

1. INTRODUCTION……………………………………………………………………...1

1.1 Introduction……………………………………………………………………1

1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………………………...2

1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………………………….....3

1.4 Literature Survey………………………………………………………...……4

1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . ………………………. . . 4

2. IMAGE PROCESSING TOOLBOX ….……………………………………………... 5

2.1 Introduction………………….……………………………………………..….5

2.1.1 Array in Image Processing Toolbox………………………………...5

2.1.2 Data Types…………………………………………………………..6

2.1.3 Read and Write Images………………………………………...…..10

2.2 Moving Object Detection . . . . . . . . . . . . . . . . . . . . . ……………………… . 11

2.2.1 Background Subtraction . . . . . . . . . ………………….. . . . . . . . . . 11

2.2.2 Statistical Methods . . . . . . . . . . ………………….... . . . . . . . . . . . 12

2.2.3 Temporal Differencing . . . . . ………………………………. . . …12

2.2.4 Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . ……………………….13

2.1.5 Shadow and Light Change Detection.……..………………………13

2.3 Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………………………..14

3. OBJECT DETECTION AND TRACKING ………………………………………….15

3.1 Introduction…………………………………………………………………..15

3.2 Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . ………………………...16

VII

3.2.1 Background Subtraction . . . . . . . . . . . . . . . . . . . . . ………………18

3.2.2 Pixel Level Post-Processing . . . . . . . . . . . . . . . . . ………………..19

3.2.3 Detecting Connected Regions . . . . . . . . . . . . . . . . ………………..26

3.2.4 Region Level Post-Processing . . . . . . . . . . . . . . . . ……………….26

3.3 Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………………………..26

3.3.1 Correspondence-based Object Matching . . . . . . . . . . …………….27

3.3.2 Occlusion Handling . . . . . . . . . . . . . . . . . . . . . …………………...32

3.3.3 Morphological Filters………………………………………………33

3.3.4 Detecting Left and Removed Objects . . . . . . . . . . . . ...…………...37

4. SOFTWARE DESCRIPTION………….…...……….………………………………..39

4.1 Introduction…………………………………………………………………..39

4.2 Data Manipulations………………………………………………………......41

4.3 Using Help…………………………………………………………………...43

4.4 How to Save………………...………………………………………………..45

5. EXPERIMENTAL RESULTS……………….

……………………………………….48

5.1 Test Application and System . . . . . . . . . . . . . . . . . . . . . ……………………48

5.2 Object Detection and Tracking . . . . . . . . . . . . . . . . . . …………………….. 48

5.3 Object Classification . . . . . . . . . . . . . . . . . . . . . . . . . ……………………….49

6. CONCLUSION AND FUTURE SCOPE……………………………………………..52

Bibliography …………………………………………………………………………….53

Appendix………………………………………………………………………………....54

List of Figures2.1 Indexed Image………………………………………………………………………...7

2.2 Intensity Image………………………………………………………………………...8

2.3 Binary Image………………………………………………………………………..…9

2.4 RGB Image…………………………………………………………………………..10

2.5 A generic framework for smart video processing algorithms. . . . …………………. 11

3.1 The system block diagram. . . . . . . . . . . . . . . . . . . . . . . …………………………….15

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3.2 The object detection system diagram. . . . . . . . . . . . . . . . . ………………………….17

3.3 Adaptive Background Subtraction sample. (a) Estimated background

(b) Current image (c) Detected region . . . . . . . . . . . ………………………………..19

3.4 Pixel level noise removal sample. (a) Estimated background image

(b) Current image (c) Detected foreground regions before noise

Removal (d) Foreground regions after noise removal . . . .. . . …………………… 21

3.5 Shadow removal sample. (a) Estimated background (b) Current

image (c) Detected foreground pixels (shown as red) and shadow

pixels (shown as green) (d) Foreground pixels after shadow pixels

are removed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………………………………....23

3.6 Sudden light change sample. (a) The scene before sudden light

change (b) The same scene after sudden light change . . . . …………………………23

3.7 Detecting true light change. (a) Estimated reference background

(b) Background’s gradient (c) Current image (d) Current image’s

gradient (e) The gradient difference . . . . . .. . . . . . . . ……………………………...25

3.8 Connected component labeling sample. (a) Estimated background

(b) Current image (c) Filtered foreground pixels and connected

and labeled regions with bounding boxes . . . . . . . . . . . …………………………….25

3.9 The object tracking system diagram. . . . . . . . . . . . . . . . . …………………………..27

3.10 The correspondence-based object matching method. . . . . . . . . …………………...28

3.11 Sample object matching graph. . . . . . . . . . . . . . . . . . . …………………………… 28

3.12 Occlusion detection sample case. . . . . . . . . . . . . . . . . . . ……………………….... 31

3.13 Shadow removal sample. (a) Estimated background (b) Current image

(c) Detected foreground pixels (shown as red) and

shadow pixels (shown as green)(d) Noise are removed due to

Morphological Filter………………………………………………………...…….36

3.14 Distinguishing left and removed objects. (a) Scene background (b)

Regions R and S (c) Left object sample (d) Removed object sample ……………..38

5.1 Sample video frames before and after occlusions……………………………...…....50

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List of Tables5.1 Performance of object detection algorithms . . . . . . . . . . . ………………………… 48

5.2 Occlusion handling results for sample clips . . . . . . . . . ……………………….. . . . 49

5.3 Number of object types in the sample object template database . ……………….….50

5.4 Confusion matrix for object classification . . . . . . . . . . . . . . ………………………..51

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Object detecton and tracking

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