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.
表中的内容
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.
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