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Towards Modelling Behaviour

  • Shaogang Gong
  • Tao Xiang

Abstract

Automatic interpretation of object behaviour requires constructing computational models of behaviour. In particular, it is desirable to automatically learn behaviour models directly from visual observations. In order for a computer to learn a behaviour model from data, one needs to select a suitable representation, develop a robust interpretation mechanism, and adopt an effective strategy for model learning. In this chapter, we introduce four different approaches to behaviour representation from visual data: object-based, part-based, pixel-based, and event-based representations. Behavioural interpretation of activities is commonly treated as a problem of reasoning spatio-temporal correlations and causal relationships among temporal processes in a multivariate space within which activities are represented. In this chapter, we introduce a statistical learning approach, in particular probabilistic graphical models, to underpinning the mechanism for behavioural interpretation. A statistical behaviour model is learned from training data. In this chapter, we overview different learning strategies for building behaviour models, ranging from supervised learning, unsupervised learning, semisupervised learning, weakly supervised learning, to active learning.

Keywords

Hide Markov Model Optical Flow Probabilistic Latent Semantic Analysis Probabilistic Graphical Model Optical Flow Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  2. 2.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK

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