Multi-target tracking by non-linear motion patterns based on hierarchical network flows

We propose a multi-target tracking method for pedestrians, through locating them and maintaining their identities to make their individual trajectories. In most existing data association-based tracking approaches, the motion information in the affinity model is always linear, but the motion of target is non-linear in real-world surveillance video, especially when there is occlusion. This paper proposes a global optimization method based on hierarchical network flows with a non-linear motion model to track multiple targets. In our method, each node in network represents a tracklet, and each edge represents the likelihood of neighboring tracklets belonging to the same trajectory measured by affinity score that we proposed, it differs from other network flow formulation whose nodes represent detection responses. A non-linear motion map is used for explaining non-linear motion pattern between neighboring tracklets and getting motion affinity when there are time gaps. We evaluate our approach on 2DMOT2015, MOT16 database, and several real surveillance videos. Experimental results demonstrate that the proposed method can achieve continuous tracking trajectories under the case of motion direction changes and complete occlusions.