Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object
  Tracking
Feng Yan Weixin Luo Yujie Zhong Yiyang Gan Lin Ma
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not
surpassed non-end-to-end tracking-by-detection methods. One potential reason is
its label assignment strategy during training that consistently binds the
tracked objects with tracking queries and then assigns the few newborns to
detection queries. With one-to-one bipartite matching, such an assignment will
yield unbalanced training, i.e., scarce positive samples for detection queries,
especially for an enclosed scene, as the majority of the newborns come on stage
at the beginning of videos. Thus, e2e-MOT will be easier to yield a tracking
terminal without renewal or re-initialization, compared to other
tracking-by-detection methods. To alleviate this problem, we present Co-MOT, a
simple and effective method to facilitate e2e-MOT by a novel coopetition label
assignment with a shadow concept. Specifically, we add tracked objects to the
matching targets for detection queries when performing the label assignment for
training the intermediate decoders. For query initialization, we expand each
query by a set of shadow counterparts with limited disturbance to itself. With
extensive ablations, Co-MOT achieves superior performance without extra costs,
e.g., 69.4% HOTA on DanceTrack and 52.8% TETA on BDD100K. Impressively, Co-MOT
only requires 38\% FLOPs of MOTRv2 to attain a similar performance, resulting
in the 1.4$\times$ faster inference speed.