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Anatomy of a multicamera video surveillance system

  • Sp.lss. on Video Surveillance
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Abstract.

We present a framework for multicamera video surveillance. The framework consists of three phases: detection, representation, and recognition. The detection phase handles multisource spatiotemporal data fusion for efficiently and reliably extracting motion trajectories from video. The representation phase summarizes raw trajectory data to construct hierarchical, invariant, and content-rich descriptions of the motion events. Finally, the recognition phase deals with event classification and identification on the data descriptors. Through empirical study in a parking-lot-surveillance setting, we show that our spatiotemporal fusion scheme and biased sequence-data learning method are highly effective in identifying suspicious events.

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Authors and Affiliations

  1. Computer Science, University of California, CA 93106, Santa Barbara, USA

    Long Jiao & Yuan-Fang Wang

  2. Electrical & Computer Engineering, University of California, CA 93106, Santa Barbara, USA

    Yi Wu, Gang Wu & Edward Y. Chang

Authors
  1. Long Jiao
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  2. Yi Wu
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  3. Gang Wu
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  4. Edward Y. Chang
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  5. Yuan-Fang Wang
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Corresponding author

Correspondence to Long Jiao.

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Published online: 11 October 2004

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Jiao, L., Wu, Y., Wu, G. et al. Anatomy of a multicamera video surveillance system. Multimedia Systems 10, 144–163 (2004). https://doi.org/10.1007/s00530-004-0147-2

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