Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses

This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a par-ticle filter with observer selection that could deal with par-tial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with am-biguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian al-gorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two chal-lenging datasets. The experimental results prove the effec-tiveness of our method. 1