Abstract-In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian mixture probability hypothesis density (GM-PHD) filter framework. The proposed fusion approach consists essentially of three steps. First, we integrate two human detectors with different characteristics (full-body and body-parts), and investigate their complementary benefits for tracking multiple targets. For each detector domain, we then propose a novel discriminative correlation matching model, and fuse it with spatiotemporal information to address ambiguous identity association in the GM-PHD filter. Finally, we develop a robust fusion center with virtual and real zones to make a global decision based on preliminary candidate targets generated by each detector. This center also mitigates the sensitivity of missed detections in the generalized covariance intersection fusion process, thereby improving the fusion performance and tracking consistency. Experiments on the MOTChallenge Benchmark demonstrate that the proposed method achieves improved performance over other state-of-the-art RFS-based tracking methods. Index Terms-Multiple human tracking, GM-PHD filter, data fusion.
In REF , object position is predicted in a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which is used with a size based location measure in discriminative correlation based appearance cost calculation.
Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Computer Science