  Joint Detection and Embedding (JDE) trackers have demonstrated excellent
performance in Multi-Object Tracking (MOT) tasks by incorporating the
extraction of appearance features as auxiliary tasks through embedding
Re-Identification task (ReID) into the detector, achieving a balance between
inference speed and tracking performance. However, solving the competition
between the detector and the feature extractor has always been a challenge.
Meanwhile, the issue of directly embedding the ReID task into MOT has remained
unresolved. The lack of high discriminability in appearance features results in
their limited utility. In this paper, a new learning approach using
cross-correlation to capture temporal information of objects is proposed. The
feature extraction network is no longer trained solely on appearance features
from each frame but learns richer motion features by utilizing feature heatmaps
from consecutive frames, which addresses the challenge of inter-class feature
similarity. Furthermore, our learning approach is applied to a more lightweight
feature extraction network, and treat the feature matching scores as strong
cues rather than auxiliary cues, with an appropriate weight calculation to
reflect the compatibility between our obtained features and the MOT task. Our
tracker, named TCBTrack, achieves state-of-the-art performance on multiple
public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically,
on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA,
making it the best online tracker capable of achieving real-time performance.
Comparative evaluations with other trackers prove that our tracker achieves the
best balance between speed, robustness and accuracy. Code is available at
https://github.com/yfzhang1214/TCBTrack.
