Pedestrian Detection and Tracking for Counting Applications in Crowded Situations
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
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This paper proposes a method to automatically count the number of pedestrians in a video input of a crowed scene. The method proposed in this paper improves on our previous pedestrian counting method which estimates the number of pedestrians by accumulating low-level features (foreground pixels and motion vectors) on a virtual gate. To handle crowded scenes, the pedestrian counting process in this paper is weighted by the ratio of foreground pixels in the scene. The relationship between crowdedness and weighting factor is learned from 10,000 simulation images. Tests on real video sequences show that this method can successfully estimate the number of pedestrians with an accuracy of about 95%. Also, when compared to the previous method, the accuracy was increased by about 5% for highly crowded scenes. Moreover, the proposed method runs at an average rate of around 60 fps on a standard PC, which makes the algorithm realistic for multi-camera systems.