Segment-wise online learning based on greedy algorithm for real-time multi-target tracking

This paper proposes a tracklet-based algorithm for online multiple-target tracking. The algorithm performs tracking in three steps: (1) tracklet initialization, (2) tracklet refinement, and (3) tracklet association. Given detection responses, tracklets are initialized by finding a near-optimum path in the min-cost flow network using a greedy-based algorithm. Based on an appearance-based model, the tracklets are refined so that the detection responses within the tracklet become more homogeneous. Finally, the tracklets are linked based on a novel affinity measure, then by optimizing a min-cost flow network with links, the final tracks are generated. For real-time multi-target tracking, every step is processed in a segment-wise manner. On popular public datasets and strictly in an online fashion, the proposed multi-target tracking algorithm performed comparable to that of many state-of-the-art algorithms.