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Open Access Publications from the University of California

A Machine Vision Based Surveillance System For California Roads


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.

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