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Deformable part model based multiple pedestrian detection for video surveillance in crowded scenes

Pedestrian detection is a challenging task for video surveillance. The problem becomes more difficult when occlusion is prevalent. In this paper, we extend a deformable part-based pedestrian detector to pedestrian detection in crowded scenes by considering both body part detection responses and detections' mutual spatial relationship. Specifically, we first decompose the full body detector into several body part detectors, whose detection responses can be computed efficiently from the response of the full body detector. Then, given the detection responses of the body part detectors, hypotheses are nominated by considering both detection scores and responses' mutual spatial relationship. Finally, a local optimization process is applied to make the final decision, where an objective function encouraging detections with high confidence, high discriminability and low conflict with other detections is proposed to select the best candidate detections. Experimental results show the effectiveness of the proposed approach.