- Full 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.
- Table of Contents
Table of Contents
- 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.
- Camera Behaviour Correlation.
- Person Re
- Connecting the Dots.
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