  Recent Multiple Object Tracking (MOT) methods have gradually attempted to
integrate object detection and instance re-identification (Re-ID) into a united
network to form a one-stage solution. Typically, these methods use two
separated branches within a single network to accomplish detection and Re-ID
respectively without studying the inter-relationship between them, which
inevitably impedes the tracking performance. In this paper, we propose an
online multi-object tracking framework based on a hierarchical single-branch
network to solve this problem. Specifically, the proposed single-branch network
utilizes an improved Hierarchical Online In-stance Matching (iHOIM) loss to
explicitly model the inter-relationship between object detection and Re-ID. Our
novel iHOIM loss function unifies the objectives of the two sub-tasks and
encourages better detection performance and feature learning even in extremely
crowded scenes. Moreover, we propose to introduce the object positions,
predicted by a motion model, as region proposals for subsequent object
detection, where the intuition is that detection results and motion predictions
can complement each other in different scenarios. Experimental results on MOT16
and MOT20 datasets show that we can achieve state-of-the-art tracking
performance, and the ablation study verifies the effectiveness of each proposed
component.
