Tracking with Particle Filter for High-dimensional Observation and State Spaces (eBook)

Séverine Dubuisson, Associate Professor, Lip6, UPMC, Paris, France.

NOTATIONS ix

INTRODUCTION xi

CHAPTER 1. VISUAL TRACKING BY PARTICLE FILTERING 1

1.1. Introduction 1

1.2. Theoretical models 2

1.2.1. Recursive Bayesian filtering 2

1.2.2. Sequential Monte-Carlo methods 4

1.2.3. Application to visual tracking 8

1.3. Limits and challenges 18

1.4. Scientific position 22

1.5. Managing large sizes in particle filtering 22

1.6. Conclusion 26

CHAPTER 2. DATA REPRESENTATION MODELS 29

2.1. Introduction 29

2.2. Computation of the likelihood function 30

2.2.1. Exploitation of the spatial redundancy 31

2.2.2. Exploitation of the temporal redundancy 42

2.3. Representation of complex information 50

2.3.1. Representation of observations for movement detection, appearances and disappearances 50

2.3.2. Representation of deformations 53

2.3.3. Multifeature representation 56

2.4. Conclusion 75

CHAPTER 3. TRACKING MODELS THAT FOCUS ON THE STATE SPACE 79

3.1. Introduction 79

3.2. Data association methods for multi-object tracking 80

3.2.1. Particle filter with adaptive classification 84

3.2.2. Energetic filter for data association 87

3.3. Introducing fuzzy information into the particle filter 95

3.3.1. Fuzzy representation 96

3.3.2. Fuzzy spatial relations 98

3.3.3. Integration of fuzzy spatial relations into the particle filter 99

3.4. Conjoint estimation of dynamic and static parameters 114

3.5. Conclusion 119

CHAPTER 4. MODELS OF TRACKING BY DECOMPOSITION OF THE STATE SPACE 123

4.1. Introduction 123

4.2. Ranked partitioned sampling 126

4.3. Weighted partitioning with permutation of sub-particles 133

4.3.1. Permutation of sub-samples 135

4.3.2. Decrease the number of resamplings 138

4.3.3. General algorithm and results 138

4.4. Combinatorial resampling 142

4.5. Conclusion 149

CHAPTER 5. RESEARCH PERSPECTIVES IN TRACKING AND MANAGING LARGE SPACES 151

5.1. Tracking for behavioral analysis: toward finer tracking of the "future" and the "now" 153

5.2. Tracking for event detection: toward a top-down model 156

5.3. Tracking to measure social interactions 159

BIBLIOGRAPHY 163

INDEX 197