As a video task, Multi-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
\href{https://github.com/MCG-NJU/MeMOTR}{this https URL}.

我们提出了 MeMOTR，一种用于多目标跟踪的长期记忆增强 Transformer 模型，通过使用自定义的记忆 - 注意力层注入长期记忆，使得相同目标的轨迹嵌入更加稳定和可分辨，显著提高了模型的目标关联能力。实验结果显示，MeMOTR 在 HOTA 和 AssA 指标上分别超过了现有方法的 7.9％和 13.0％，并且在 MOT17 上的关联性能也优于其他基于 Transformer 的方法，同时在 BDD100K 上也具有很好的泛化性能。