Development and evaluation of real-time video surveillance system on highway based on semantic hierarchy and decision surface

In this paper, an automated incident detection system for traffic on multilane straight roadways is presented. Utilizing ST-MRF based tracking algorithm, this system detects slow vehicles, congestion and incidents including stalled vehicles. ST-MRF based tracking algorithm is designed for robustness against occlusion and illumination changes regardless of camera angle. Incident detection algorithm consists of multiple operators of three classes: coordinate-class, behavior-class, and event-class. Constructing the semantic hierarchy composed of these classes enables the system to have a natural interface with human traffic control experts. We evaluated the efficiency of this system with 30 months real traffic data in total at two different locations, in which it performed with more than 95% recall rate.