Instance Segmentation Enabled Hybrid Data Association and Discriminative Hashing for Online Multi-Object Tracking

Online multi-object tracking remains a difficult problem in complex scenes because of inaccurate detections, frequent occlusions by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a hybrid data association strategy combined with instance segmentation and online feature learning to handle these difficulties effectively. First, we utilize an instance segmentation algorithm to separate each object from other objects and backgrounds in a pixel-to-pixel manner, which will help resolve typical difficulties in multi-object tracking, such as ID-switches and track drifting. Moreover, we propose a local-to-global hybrid data association strategy to take advantage of the superiorities of both online and batch data association methods. The local data association between observations in consecutive frames reduces the computational complexity, hence ensuring the efficiency of online tracking. The global data association complements the local data association by integrating multiple video frames, thus alleviating the fragmented tracklets. Last, to improve the appearance discriminability and make it more robust in dealing with appearance variations during tracking, a lightweight semantic-preserving hashing algorithm has been proposed to learn compact hash codes online. Experiments with the MOT17 Challenge dataset demonstrate the superior performance of the proposed approach over other state-of-the-art batch and online tracking methods.