Data Association Based Multi-target Tracking Using a Joint Formulation

We revisit the classical conditional random filed based tracking-by-detection framework for multi-target tracking, in which function factors associating pairs of short tracklets in a long term are modeled to produce final tracks. Unlike most previous approaches which only focus on modeling feature difference for distinguishing pairs of targets, we propose to directly model the joint formulation of pairs of tracklets for association in the CRF framework. To this end, we use a Hough Forest (HF) based learning framework to effectively learn a discriminative codebook of features among tracklets by utilizing appearance and motion cues stored in the leaf nodes. Given the learned codebook, the joint formulation of tracklet pairs can be directly modeled in a nonparametric manner by defining a sharing and excluding matrix. Then all of the statistics required in CRF inference can be directly estimated. Extensive experiments have been conducted on several public datasets, and the performance is comparable to the state of the art.