Dual-Direction Perception and Collaboration Network for Near-Online Multi-Object Tracking

Backward tracks have been exploited to improve performance of multi-object tracking (MOT). The existing method brings a stable similarity measurement but neglects unreliable detection. Exploiting predictions of forward tracks has emerged as a popular approach to tackle the task of tracking-by-detection. However, it's observed that missing detection has not been solved well enough which would significantly influence the tracking accuracy. Thus, obtaining more proposals from dual-direction tracking and predictions of tracks is concerned to address the problem of missing detection. In this paper, we propose a dual-direction perception and collaboration network (DPCNet) for MOT that exploits forward and backward tracking to collaboratively track objects. It collects candidates from the dual directions so that they can complement each other in different scenarios. Moreover, we propose a near-online tracking model based on DPCNet to improve the efficiency, which batches the tracking and makes forward and backward tracking in parallel. Experiments conducted on MOT challenge benchmarks demonstrate that the proposed method outperforms the state-of-the-arts.