  Due to balanced accuracy and speed, one-shot models which jointly learn
detection and identification embeddings, have drawn great attention in
multi-object tracking (MOT). However, the inherent differences and relations
between detection and re-identification (ReID) are unconsciously overlooked
because of treating them as two isolated tasks in the one-shot tracking
paradigm. This leads to inferior performance compared with existing two-stage
methods. In this paper, we first dissect the reasoning process for these two
tasks, which reveals that the competition between them inevitably would destroy
task-dependent representations learning. To tackle this problem, we propose a
novel reciprocal network (REN) with a self-relation and cross-relation design
so that to impel each branch to better learn task-dependent representations.
The proposed model aims to alleviate the deleterious tasks competition,
meanwhile improve the cooperation between detection and ReID. Furthermore, we
introduce a scale-aware attention network (SAAN) that prevents semantic level
misalignment to improve the association capability of ID embeddings. By
integrating the two delicately designed networks into a one-shot online MOT
system, we construct a strong MOT tracker, namely CSTrack. Our tracker achieves
the state-of-the-art performance on MOT16, MOT17 and MOT20 datasets, without
other bells and whistles. Moreover, CSTrack is efficient and runs at 16.4 FPS
on a single modern GPU, and its lightweight version even runs at 34.6 FPS. The
complete code has been released at https://github.com/JudasDie/SOTS.
