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E-raamat: Video Tracking: Theory and Practice

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(Queen Mary, University of London), (Vicon)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Dec-2010
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470974384
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Video Tracking: Theory and PracticeSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Dec-2010
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9780470974384
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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

Arvustused

"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)

Foreword xi
About the authors xv
Preface xvii
Acknowledgements xix
Notation xxi
Acronyms xxiii
1 What is video tracking?
1(14)
1.1 Introduction
1(1)
1.2 The design of a video tracker
2(5)
1.2.1 Challenges
2(4)
1.2.2 Main components
6(1)
1.3 Problem formulation
7(5)
1.3.1 Single-target tracking
7(3)
1.3.2 Multi-target tracking
10(1)
1.3.3 Definitions
11(1)
1.4 Interactive versus automated tracking
12(1)
1.5 Summary
13(2)
2 Applications
15(12)
2.1 Introduction
15(1)
2.2 Media production and augmented reality
16(1)
2.3 Medical applications and biological research
17(3)
2.4 Surveillance and business intelligence
20(1)
2.5 Robotics and unmanned vehicles
21(1)
2.6 Tele-collaboration and interactive gaming
22(1)
2.7 Art installations and performances
22(1)
2.8 Summary
23(4)
References
24(3)
3 Feature extraction
27(44)
3.1 Introduction
27(1)
3.2 From light to useful information
28(4)
3.2.1 Measuring light
28(2)
3.2.2 The appeamnce of targets
30(2)
3.3 Low-level features
32(18)
3.3.1 Colour
32(7)
3.3.2 Photometric colour invariants
39(3)
3.3.3 Gradient and derivatives
42(5)
3.3.4 Laplacian
47(2)
3.3.5 Motion
49(1)
3.4 Mid-level features
50(11)
3.4.1 Edges
50(1)
3.4.2 Interest points and interest regions
51(5)
3.4.3 Uniform regions
56(5)
3.5 High-level features
61(4)
3.5.1 Background models
62(1)
3.5.2 Object models
63(2)
3.6 Summary
65(6)
References
65(6)
4 Target representation
71(18)
4.1 Introduction
71(1)
4.2 Shape representation
72(3)
4.2.1 Basic models
72(1)
4.2.2 Articulated models
73(1)
4.2.3 Deformable models
74(1)
4.3 Appearance representation
75(9)
4.3.1 Template
76(2)
4.3.2 Histograms
78(5)
4.3.3 Coping with appearance changes
83(1)
4.4 Summary
84(5)
References
85(4)
5 Localisation
89(26)
5.1 Introduction
89(1)
5.2 Single-hypothesis methods
90(8)
5.2.1 Gradient-based trackers
90(5)
5.2.2 Bayes tracking and the Kalman filter
95(3)
5.3 Multiple-hypothesis methods
98(13)
5.3.1 Grid sampling
99(2)
5.3.2 Particle filter
101(4)
5.3.3 Hybrid methods
105(6)
5.4 Summary
111(4)
References
111(4)
6 Fusion
115(16)
6.1 Introduction
115(1)
6.2 Fusion strategies
116(3)
6.2.1 Tracker-level fusion
118(1)
6.2.2 Measurement-level fusion
118(1)
6.3 Feature fusion in a Particle Filter
119(9)
6.3.1 Fusion of likelihoods
119(2)
6.3.2 Multi-feature resampling
121(2)
6.3.3 Feature, reliability
123(3)
6.3.4 Temporal smoothing
126(1)
6.3.5 Example
126(2)
6.4 Summary
128(3)
References
128(3)
7 Multi-target management
131(38)
7.1 Introduction
131(1)
7.2 Measurement validation
132(2)
7.3 Data association
134(9)
7.3.1 Nearest neighbour
134(2)
7.3.2 Graph matching
136(3)
7.3.3 Multiple-hypothesis tracking
139(4)
7.4 Random Finite Sets for tracking
143(2)
7.5 Probabilistic Hypothesis Density filter
145(2)
7.6 The Particle PHD filter
147(16)
7.6.1 Dynamic and observation models
149(2)
7.6.2 Birth and clutter models
151(1)
7.6.3 Importance sampling
151(1)
7.6.4 Resampling
152(4)
7.6.5 Particle clustering
156(4)
7.6.6 Examples
160(3)
7.7 Summary
163(6)
References
165(4)
8 Context modeling
169(16)
8.1 Introduction
169(1)
8.2 Tracking with context modelling
170(3)
8.2.1 Contextual information
170(1)
8.2.2 Influence of the context
171(2)
8.3 Birth and clutter intensity estimation
173(11)
8.3.1 Birth density
173(6)
8.3.2 Clutter density
179(2)
8.3.3 Tracking with contextual feedback
181(3)
8.4 Summary
184(1)
References
184(1)
9 Performance evaluation
185(38)
9.1 Introduction
185(1)
9.2 Analytical versus empirical methods
186(1)
9.3 Ground truth
187(3)
9.4 Evaluation scores
190(6)
9.4.1 Localisation scores
190(3)
9.4.2 Classification scores
193(3)
9.5 Comparing trackers
196(3)
9.5.1 Target life-span
197(1)
9.5.2 Statistical significance
198(1)
9.5.3 Rcpeatibility
198(1)
9.6 Evaluation protocols
199(8)
9.6.1 Low-level protocols
199(4)
9.6.2 High-level protocols
203(4)
9.7 Datasets
207(13)
9.7.1 Surveillance
207(5)
9.7.2 Human-computer interaction
212(3)
9.7.3 Sport analysis
215(5)
9.8 Summary
220(3)
References
220(3)
Epilogue
223(2)
Further reading
225(4)
Appendix A Comparative results
229(34)
A.1 Single versus structural histogram
229(4)
A.1.1 Experimental setup
229(1)
A.1.2 Discussion
230(3)
A.2 Localisation algorithms
233(5)
A.2.1 Experimental setup
233(2)
A.2.2 Discussion
235(3)
A.3 Multi-feature fusion
238(10)
A.3.1 Experimental setup
238(2)
A.3.2 Reliability scores
240(2)
A.3.3 Adaptive versus non-adaptive tracker
242(6)
A.3.4 Computational complexity
248(1)
A.4 PHD filter
248(9)
A.4.1 Experimental setup
248(2)
A.4.2 Discussion
250(1)
A.4.3 Failure modalities
251(4)
A.4.4 Computational cost
255(2)
A.5 Context modelling
257(6)
A.5.1 Experimental setup
257(1)
A.5.2 Discussion
257(4)
References
261(2)
Index 263
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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