Description
This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning; examines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines. Part I: Introduction About Behaviour Behaviour in Context Towards Modelling Behaviour Part II: Single-Object Behaviour Understanding Facial Expression Modelling Gesture Action Recognition Part III: Group Behaviour Supervised Learning of Group Activity Unsupervised Behaviour Profiling Hierarchical Behaviour Discovery Learning Behavioural Context Modelling Rare and Subtle Behaviours Man in the Loop Part IV: Distributed Behaviour Multi-Camera Behaviour Correlation Person Re-Identification Connecting the Dots Epilogue
