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Demand continues to cultivate worldwide, from both government and commerce, for technologies effective at automatically selecting and identifying object and human behaviour. This accessible text/reference presents an all-inclusive and unified treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. The book provides in-depth discussion on computer vision and statistical machine learning techniques, as well as reviewing a diverse variety of behaviour modelling problems. A mathematical background isn't needed to understand the content, although readers will take advantage of modest familiarity with vectors and matrices, eigenvectors and eigenvalues, linear algebra, optimisation, multivariate analysis, probability, statistics and calculus. Topics and features: Provides a thorough introduction to the study and modelling of behaviour, plus a concluding epilogueCovers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and “man-in-the-loop” active learning of behavioursExamines multi-camera behaviour correlation, person re-identification, and “connecting-the-dots” for global abnormal behaviour detectionDiscusses Bayesian information criterion, static Bayesian graph models, “bag-of-words” representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs samplingInvestigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processesExplores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machinesIncludes a helpful list of acronymsA valuable resource for both researchers in computer vision and machine learning, and then for developers of business applications, the novel can also function as useful reference for postgraduate students of computer science and behavioural science. Furthermore, policymakers and commercial managers will find this an educated guide on intelligent video analytics systems. Dr. Shaogang Gong can be a Professor of Visual Computation inside School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. Dr. Tao Xiang can be a Lecturer with the same institution.