Tracking with Particle Filter for High-dimensional Observation and State Spaces [Hardcover]

This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces.  Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions.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 79l3: