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}{https://github.com/MCG-NJU/MeMOTR}.

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。

MeMOTR: 长期记忆增强转换器用于多目标跟踪