Hierarchical Group Structures in Multi-Person Tracking

This paper presents a novel approach for improving multi-person tracking using hierarchical group structures. The groups are identified by a bottom-up social group discovery method. The inter-and intra-group structures are modeled as a two-layer graph and tracking is posed as optimization of the integrated structure. The target appearance is modeled using HOG features, and the tracking solution is obtained via dynamic programming. The group structures are updated continuously and re-initialized intermittently using collected tracking evidence. We test our method on videos from four challenging datasets and evaluate it against state-of-the-art trackers. The significant performance improvement shows the importance of modeling the intra-group relationships and the advantage of the two-layer graph structure.