Monitoring crowded traffic scenes

This paper deals with real-time image processing of crowded outdoor scenes with the objective of creating an effective traffic management system that monitors urban settings (urban intersections, streets after athletic events, etc.). The proposed system can detect, track, and monitor both pedestrians (crowds) and vehicles. We describe the characteristics of the tracker that is based on a new detection method. Initially, we produce a motion estimation map. This map is then segmented and analyzed in order to remove inherent noise and focus on particular regions. Moreover, tracking of these regions is obtained in two steps: fusion and measurement of the current position and velocity, and then estimation of the next position based on a simple model. The instability of tracking is addressed by a multiple-level approach to the problem. The computed data are then analyzed to produce motion statistics. Experimental results from various sites in the Twin Cities area are presented. The final step is to provide this information to an urban traffic management center that monitors crowds and vehicles in the streets.