Self-supervised multi-object trackers have the potential to leverage the vast
amounts of raw data recorded worldwide. However, they still fall short in
re-identification accuracy compared to their supervised counterparts. We
hypothesize that this deficiency results from restricting self-supervised
objectives to single frames or frame pairs. Such designs lack sufficient visual
appearance variations during training to learn consistent re-identification
features. Therefore, we propose a training objective that learns
re-identification features over a sequence of frames by enforcing consistent
association scores across short and long timescales. Extensive evaluations on
the BDD100K and MOT17 benchmarks demonstrate that our learned ReID features
significantly reduce ID switches compared to other self-supervised methods,
setting the new state of the art for self-supervised multi-object tracking and
even performing on par with supervised methods on the BDD100k benchmark.

通过在图像序列帧上进行一致的关联性评分，我们提出了一种学习重新识别特征的训练目标，可以显著降低 ID 开关，即使在 BDD100k 基准测试中表现和受监督的方法相当，也能够设置自监督多目标跟踪的新颖技术。