Tracklet association for object tracking

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 PETS 2009/2010 and TUD-Stadtmitte database 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.