A Machine Vision Based Surveillance System For California Roads

Automatic symbolic tra c scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Tra c scene information can be used to optimize tra c ow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable tra c scene analysis. The machine vision component of our system employs a correlation-based tracker and a physical motion model using a Kalman lter to extract vehicle trajectories over a sequence of tra c scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about tra c events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype. Preliminary results of an implementation on special purpose hardware using C-40 Digital Signal Processors show that near real-time performance can be achieved without further improvements. keywords: Video Image Processing, Tra c Surveillance, PATH, Incident Detection, Computer Vision, Automatic Vehicle Classi cation, Arti cial Intelligence, Advanced Tra c Management Systems.