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Visual Analysis of Behaviour pp 39–65Cite as

Towards Modelling Behaviour

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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.

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Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, UK

    Shaogang Gong

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, UK

    Tao Xiang

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  1. Shaogang Gong
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  2. Tao Xiang
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Correspondence to Shaogang Gong .

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Gong, S., Xiang, T. (2011). Towards Modelling Behaviour. In: Visual Analysis of Behaviour. Springer, London. https://doi.org/10.1007/978-0-85729-670-2_3

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