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Hierarchical Behaviour Discovery

  • Shaogang Gong
  • Tao Xiang

Abstract

Behaviour of groups of objects observed in a crowded public space is typically complex and uncertain. What is considered to be ‘subjectively interesting behaviour’ to a human observer can be influenced by a wide variety of factors including: (1) the activity of a single object over time; (2) the correlated spatial states of multiple objects, for example, a piece of abandoned luggage is defined by separation from its owner; and (3) higher order spatial and temporal correlations among multiple entities, for instance, traffic flow at a road intersection has a particular spatio-temporal order beyond co-occurrence dictated by traffic lights. Constructing computational models that are both flexible and accurate in representing such complex and uncertain characteristics of behaviour is challenging. A dynamic topic model possesses unique computational attributes that make it an attractive framework for addressing these challenges. In this chapter, we describe a Markov clustering topic model designed for unsupervised modelling and on-line processing of multi-object spatio-temporal behaviours in crowded public scenes. A Markov clustering topic model draws on machine learning theories on probabilistic topic models and dynamic Bayesian networks to achieve a robust hierarchical modelling of behaviours and their dynamics.

Keywords

Topic Model Latent Dirichlet Allocation Saliency Detection Behaviour Class Behaviour Cluster 
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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