A Particle Filter based Multi-person Tracking with Occlusion Handling
Ruixing Yu, Bing Zhu, Wenfeng Li, Xianglong Kong · 2016
A multi-person tracking method is proposed concerning how to conquer the difficulties such as occlusion and changes in appearance which makes algorithm hard to get the correct positions of object. First, we indicate whether the target is blocked or not, through computing the Reliability of Tracklets (RT) based on the length of tracklets, appearance affinity and the size. Then, we propose a “correct” observation sample selection method and only update the weights of particle filter when the RT is high. Last, the greedy bipartite algorithm is used to realize data association. Experiments show that tracking can be successfully achieved even under severe occlusion.