Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context

An online approach to learn elementary groups containing only two targets, i.e., pedestrians, for inferring high-level context is introduced to improve multiperson tracking. In most existing data association-based tracking approaches, only low-level information (e.g., time, appearance, and motion) is used to build the affinity model, and each target is considered as an independent agent. Unlike those previous methods, in this paper, an online learned social grouping behavior model is used to provide more robust tracklet affinities. A disjoint grouping graph is used to encode social grouping behavior of pairwise targets, where each node represents an elementary group of two targets, and two nodes are connected if they share a common target. Probabilities of the uncertain target in two connected nodes being the same person are inferred from each edge of the grouping graph. Relationships between elementary groups are discovered by group tracking, and a nonlinear motion map is used for explaining nonlinear motion pattern between elementary groups. The proposed method is efficient, able to handle group split and merge, and can be easily integrated into any basic affinity model. The approach is evaluated on four data sets, and it shows significant improvements compared with state-of-the-art methods.