Tracklet association based multi-target tracking

Abstract

This paper proposes a novel multi-target tracking framework, where two different association strategies are utilized to obtain local and global tracking trajectories. Specifically, a scene self-adaptive model is first utilized to generate local trajectories by constructing the association between detection responses and tracking tracklets; then, a novel incremental linear discriminative appearance model is utilized to generate global trajectories by constructing the association between local trajectories; finally, a non-linear motion model is utilized to fill the vacancies between global trajectories to obtain continuous and smooth tracking trajectories. Experimental results conducted on PETS2009/2010, TUD-Stadtmitte, and Town Center video libraries demonstrate the proposed framework can achieve continuous and smooth tracking trajectories under the case of significant deformation, appearance change, similar appearance, motion direction change, and long-time occlusion.