Multi-person Tracking by Multicut and Deep Matching

In Tang et al. (2015), we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem. In this paper, we modify and extend Tang et al. (2015) in three ways: (1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion. (2) We perform extensive experiments to compare different pairwise potentials and to analyze the robustness of the tracking formulation. (3) We consider a plain multicut problem and remove outlying clusters from its solution. This allows us to employ an efficient primal feasible optimization algorithm that is not applicable to the subgraph multicut problem of Tang et al. (2015). Unlike the branch-and-cut algorithm used there, this efficient algorithm used here is applicable to long videos and many detections. Together with the novel pairwise feature, it eliminates the need for the intermediate tracklet representation of Tang et al. (2015). We demonstrate the effectiveness of our overall approach on the MOT16 benchmark (Milan et al. 2016), achieving state-of-art performance.