Overview

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.

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9780857296696

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From the reviews: This book presents a comprehensive introduction to algorithms and methodologies for representing, learning, recognizing, interpreting and predicting human behaviour, on the basis of visual data. Examples of human behaviour are given by facial expression, body gesture and human action. The book is mainly intended for readers interested in applications in the fields of visual surveillance, video indexing and search, robotics and healthcare, interaction, animation and computer games. (Patrizio Frosini, Zentralblatt MATH, Vol. 1238, 2012)

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