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A Machine Vision Based Surveillance System For California Roads

Author

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  • Malik, J.
  • Russell, S.

Abstract

In this paper, the authors describe the successful combination of a low- level, vision-based surveillance system with a high-level, symbolic reasoner based on dynamic belief networks. This prototype system provides robust, high-level information about traffic scenes. The machine vision component of the system employs a correlation-based tracker and a physical motion model using a Kalman filter to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events. In this paper, the authors discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.

Suggested Citation

  • Malik, J. & Russell, S., 1995. "A Machine Vision Based Surveillance System For California Roads," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt31x0176f, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt31x0176f
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    File URL: https://www.escholarship.org/uc/item/31x0176f.pdf;origin=repeccitec
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    References listed on IDEAS

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    1. Koler, Dieter & Weber, Joseph & Malik, Jitendra, 1994. "Robust Multiple Car Tracking With Occlusion Reasoning," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt49c0g7p8, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Hall, Randolph & Mehta, Yatrik, 1998. "Incident Management: Process Analysis And Improvement Phase 1: Review Of Procedures," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt45r743q6, Institute of Transportation Studies, UC Berkeley.

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