A Machine Vision Based Surveillance System For California Roads

J. Malik and S. Russell

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

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.

Keywords: Computer vision; Image processing; Automobiles--Automatic control; Traffic surveillance; Incident detection; Automatic vehicle classification (search for similar items in EconPapers)
Date: 1995-01-01
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Citations: View citations in EconPapers (1)

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