  As a video task, Multiple Object Tracking (MOT) is expected to capture
temporal information of targets effectively. Unfortunately, most existing
methods only explicitly exploit the object features between adjacent frames,
while lacking the capacity to model long-term temporal information. In this
paper, we propose MeMOTR, a long-term memory-augmented Transformer for
multi-object tracking. Our method is able to make the same object's track
embedding more stable and distinguishable by leveraging long-term memory
injection with a customized memory-attention layer. This significantly improves
the target association ability of our model. Experimental results on DanceTrack
show that MeMOTR impressively surpasses the state-of-the-art method by 7.9% and
13.0% on HOTA and AssA metrics, respectively. Furthermore, our model also
outperforms other Transformer-based methods on association performance on MOT17
and generalizes well on BDD100K. Code is available at
https://github.com/MCG-NJU/MeMOTR.
