Modelling Rare and Subtle Behaviours


One of the most desired capabilities for automated visual analysis of behaviour is the identification of rarely occurring and subtle behaviours of genuine interest. This is of practical value because the behaviours of greatest interest for detection are normally rare, for example civil disobedience, shoplifting, driving offenses, and may be intentionally disguised to be visually subtle compared to more obvious ongoing behaviours in a public space. Rare behaviours by definition have few examples for a model to learn from. The most interesting rare behaviours are often also subtle and do not exhibit abundance of strong visual features in the data that describe them. In this chapter, we consider the problem of learning behaviour models from rare and subtle examples. By rare, we mean as few as one example. By ‘subtle’, we mean weak visual evidence. There may only be a few pixels associated with a behaviour of interest captured in video data, and a few more pixels differentiating a rare behaviour from a typical one. To eliminate the prohibitive manual labelling cost, both in time and inconsistency, required by traditional supervised methods, we describe a weakly supervised framework, in which a user needs not, or cannot, explicitly locate the target behaviours of interest in the training video data.


Typical Behaviour Latent Dirichlet Allocation Marginal Likelihood Civil Disobedience Latent Dirichlet Allocation Model 
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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