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Connecting the Dots

  • Shaogang Gong
  • Tao Xiang

Abstract

One of the ultimate goals for automated visual analysis of distributed object behaviour is to bring about a coherent understanding of partially observed uncertain sensory data from the past and present, and to ‘connect the dots’ in composing a big picture of global situational awareness for explaining away anomalies and discovering hidden patterns of significance. To that end, we consider the computational task and plausible models for modelling global behaviours and detecting global abnormal activities across distributed and disjoint multiple cameras. For constructing a global behaviour model for detecting holistic anomalies, besides model sensitivity and robustness, model tractability and scalability are of a great importance. A typical distributed camera network may consist of dozens to hundreds of cameras, many of which cover a wide-area scene of different distinctive activity semantic regions. In this chapter, we describe three different approaches to building a model for discovering and describing global behaviour patterns emerging from a distributed network of local activity regions. We examine their model characteristics for coping with uncertainty and complexity in temporal delays between activities observed in different camera views, and for maintaining a manageable computational cost and memory consumption.

Keywords

Anomaly Detection Latent Dirichlet Allocation Global Behaviour Camera View Camera Network 
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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