Online Multiple Object Tracking Using Single Object Tracker and Maximum Weight Clique Graph

Abstract

Tracking multiple objects is a challenging task in time-critical video analysis systems. In the popular tracking-by-detection framework, the core problems of a tracker are the quality of the employed input detections and the effectiveness of the data association. Towards this end, we propose a multiple object tracking method which employs a single object tracker to improve the results of unreliable detection and data association simultaneously. Besides, we utilize maximum weight clique graph algorithm to handle the optimal assignment in an online mode. In our method, a robust single object tracker is used to connect previous tracked objects to tackle the current noise detection and improve the data association as a motion cue. Furthermore, we use person re-identification network to learn the historical appearances of the tracklets in order to promote the tracker’s identification ability. We conduct extensive experiments on the MOT benchmark to demonstrate the effectiveness of our tracker.