Multiple Object Tracking Based on Robust Network Flow Model

Network flow model (NF) with the pairwise property has been paid more attention by researchers in recent years. However, the pairwise will bring a detrimental tracking performance under the occlusion case, such as the phenomenon of ID switches (IDs) and frequent appearing of fragments. Referring to the pairwise, we propose the robust network flow (RNF) model, which is based on a prior that every candidate detection has a correlative degree with the current tracking path. Based on the correlative degree, the cost of the pairwise nodes is recomputed and the best candidate detection can be found. The experimental results on four challenging video sequences show that our method is able to obtain the superior performance under the occlusion case.