Automatic symbolic traffic scene analysis is essen- tial to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy pe- riods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehi- cle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system em- ploys a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajec- tories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic be- lief network to make inferences about traffic events such as vehicle lane changes and stalls. In this pa- per, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
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