Detection Based Low Frame Rate Human Tracking

Tracking by association of low frame rate detection responses is not trivial, as motion is less continuous and hence ambiguous. The problem becomes more challenging when occlusion occurs. To solve this problem, we firstly propose a robust data association method that explicitly differentiates ambiguous tracklets that are likely to introduce incorrect linking from other tracklets, and deal with them effectively. Secondly, we solve the long-time occlusion problem by detecting inter-track relationship and performing track split and merge according to appearance similarity and occlusion order. Experiment on a challenging human surveillance dataset shows the effectiveness of the proposed method.