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Video Tracking: Theory and Practice

ISBN: 978-1-119-95686-0
292 pages
July 2011
Video Tracking: Theory and Practice (1119956862) cover image
Video Tracking provides a comprehensive treatment of the fundamental aspects of algorithm and application development for the task of estimating, over time, the position of objects of interest seen through cameras. Starting from the general problem definition and a review of existing and emerging video tracking applications, the book discusses popular methods, such as those based on correlation and gradient-descent. Using practical examples, the reader is introduced to the advantages and limitations of deterministic approaches, and is then guided toward more advanced video tracking solutions, such as those based on the Bayes’ recursive framework and on Random Finite Sets.

Key features:

  • Discusses the design choices and implementation issues required to turn the underlying mathematical models into a real-world effective tracking systems.
  • Provides block diagrams and simil-code implementation of the algorithms.
  • Reviews methods to evaluate the performance of video trackers – this is identified as a major problem by end-users.

The book aims to help researchers and practitioners develop techniques and solutions based on the potential of video tracking applications. The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programmes

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Foreword xi

About the authors xv

Preface xvii

Acknowledgements xix

Notation xxi

Acronyms xxiii

1 What is video tracking? 1

1.1 Introduction 1

1.2 The design of a video tracker 2

1.2.1 Challenges 2

1.2.2 Main components 6

1.3 Problem formulation 7

1.3.1 Single-target tracking 7

1.3.2 Multi-target tracking 10

1.3.3 Definitions 11

1.4 Interactive versus automated tracking 12

1.5 Summary 13

2 Applications 15

2.1 Introduction 15

2.2 Media production and augmented reality 16

2.3 Medical applications and biological research 17

2.4 Surveillance and business intelligence 20

2.5 Robotics and unmanned vehicles 21

2.6 Tele-collaboration and interactive gaming 22

2.7 Art installations and performances 22

2.8 Summary 23

References 24

3 Feature extraction 27

3.1 Introduction 27

3.2 From light to useful information 28

3.2.1 Measuring light 28

3.2.2 The appearance of targets 30

3.3 Low-level features 32

3.3.1 Colour 32

3.3.2 Photometric colour invariants 39

3.3.3 Gradient and derivatives 42

3.3.4 Laplacian 47

3.3.5 Motion 49

3.4 Mid-level features 50

3.4.1 Edges 50

3.4.2 Interest points and interest regions 51

3.4.3 Uniform regions 56

3.5 High-level features 61

3.5.1 Background models 62

3.5.2 Object models 63

3.6 Summary 65

References 65

4 Target representation 71

4.1 Introduction 71

4.2 Shape representation 72

4.2.1 Basic models 72

4.2.2 Articulated models 73

4.2.3 Deformable models 74

4.3 Appearance representation 75

4.3.1 Template 76

4.3.2 Histograms 78

4.3.3 Coping with appearance changes 83

4.4 Summary 84

References 85

5 Localisation 89

5.1 Introduction 89

5.2 Single-hypothesis methods 90

5.2.1 Gradient-based trackers 90

5.2.2 Bayes tracking and the Kalman filter 95

5.3 Multiple-hypothesis methods 98

5.3.1 Grid sampling 99

5.3.2 Particle filter 101

5.3.3 Hybrid methods 105

5.4 Summary 111

References 111

6 Fusion 115

6.1 Introduction 115

6.2 Fusion strategies 116

6.2.1 Tracker-level fusion 116

6.2.2 Measurement-level fusion 118

6.3 Feature fusion in a Particle Filter 119

6.3.1 Fusion of likelihoods 119

6.3.2 Multi-feature resampling 121

6.3.3 Feature reliability 123

6.3.4 Temporal smoothing 126

6.3.5 Example 126

6.4 Summary 128

References 128

7 Multi-target management 131

7.1 Introduction 131

7.2 Measurement validation 132

7.3 Data association 134

7.3.1 Nearest neighbour 134

7.3.2 Graph matching 136

7.3.3 Multiple-hypothesis tracking 139

7.4 Random Finite Sets for tracking 143

7.5 Probabilistic Hypothesis Density filter 145

7.6 The Particle PHD filter 147

7.6.1 Dynamic and observation models 149

7.6.2 Birth and clutter models 151

7.6.3 Importance sampling 151

7.6.4 Resampling 152

7.6.5 Particle clustering 156

7.6.6 Examples 160

7.7 Summary 163

References 165

8 Context modeling 169

8.1 Introduction 169

8.2 Tracking with context modelling 170

8.2.1 Contextual information 170

8.2.2 Influence of the context 171

8.3 Birth and clutter intensity estimation 173

8.3.1 Birth density 173

8.3.2 Clutter density 179

8.3.3 Tracking with contextual feedback 181

8.4 Summary 184

References 184

9 Performance evaluation 185

9.1 Introduction 185

9.2 Analytical versus empirical methods 186

9.3 Ground truth 187

9.4 Evaluation scores 190

9.4.1 Localisation scores 190

9.4.2 Classification scores 193

9.5 Comparing trackers 196

9.5.1 Target life-span 197

9.5.2 Statistical significance 198

9.5.3 Repeatibility 198

9.6 Evaluation protocols 199

9.6.1 Low-level protocols 199

9.6.2 High-level protocols 203

9.7 Datasets 207

9.7.1 Surveillance 207

9.7.2 Human-computer interaction 212

9.7.3 Sport analysis 215

9.8 Summary 220

References 220

Epilogue 223

Further reading 225

Appendix A Comparative results 229

A.1 Single versus structural histogram 229

A.1.1 Experimental setup 229

A.1.2 Discussion 230

A.2 Localisation algorithms 233

A.2.1 Experimental setup 233

A.2.2 Discussion 235

A.3 Multi-feature fusion 238

A.3.1 Experimental setup 238

A.3.2 Reliability scores 240

A.3.3 Adaptive versus non-adaptive tracker 242

A.3.4 Computational complexity 248

A.4 PHD filter 248

A.4.1 Experimental setup 248

A.4.2 Discussion 250

A.4.3 Failure modalities 251

A.4.4 Computational cost 255

A.5 Context modelling 257

A.5.1 Experimental setup 257

A.5.2 Discussion 257

References 261

Index 263

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Dr Emilio Maggio, Vicon, UK
Dr Maggio is Computer Vision Scientist at Vicon, the motion capture worldwide market leader. From 2004 – 2008 he was a Ph.D. student at the Department of Electronic Engineering, Queen Mary, University of London. In 2005 and again in 2007 he was awarded the best student paper prize at ICASSP. Dr Maggio has acted as a reviewer for the IEEE Transactions on Circuits and Systems for Video Technology, the International Journal of Image and Graphics and ACM Multimedia.

Dr Andrea Cavallaro, School of Electronic Engineering and Computer Science, Queen Mary, University of London, UK
Dr Cavallaro is Reader in Multimedia Signal Processing at Queen Mary, University of London. He is the author of more than 70 papers, including 5 book chapters. He is an elected member of the IEEE Signal Processing Society, Multimedia Signal Processing Committee. He has been a member of the organizing/ technical committee for several international conferences such as Technical Chair of EUSIPCO 08 and General Chair of the IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007), with General Chair positions being held for forthcoming 2009 conferences such as BMVC 09. He has been guest editor of several special issues, including 'Multi-sensor object detection and tracking', Signal, Image and Video Processing (Springer). Dr Cavallaro was awarded the Royal Academy of Engineering teaching prize in 2007.

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"The design methodologies discussed throughout the book provide guidelines for developers in the industry working on vision-based applications. The book may also serve as a reference for engineering and computer science graduate students involved in vision, robotics, human-computer interaction, smart environments and virtual reality programs." (Zentralblatt MATH, 2011)

"While technical, the text is clearly written and supported by exceptional illustrations." (Booknews, 1 June 2011)

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Video Tracking Visit the author's website for more information related to the book.
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