MAP-Based Online Data Association for Multiple People Tracking in Crowded Scenes

Soo Wan Kim, Moonsub Byeon, Kikyung Kim, Jin Young Choi

Abstract: This paper presents an online data association approach to handle new detection and missing detection problems of multiple people tracking in crowded scenes. The key contribution of our paper includes two aspects: one is automatic initiation of tracking models for newly appeared detections and the other is selective update of tracking models for missing detections by occlusions. For the automatic initiation, instead of the conventional matching algorithm, our data association is solved by a maximum a posteriori probability (MAP) formulation considering object's size, center distance, motion and appearance. The selective update scheme for tracking models is developed by considering the spatial information which prevents the tracking model from being corrupted with unreliable information. Even if the head detector is less discriminative due to low number of features than full body and only the recent tracking models are used for online association purpose, the proposed method shows improved performance compared to the state-of-art offline association approach with significantly low computational load.