Multiple object tracking (MOT) tends to become more challenging when severe
occlusions occur. In this paper, we analyze the limitations of traditional
Convolutional Neural Network-based methods and Transformer-based methods in
handling occlusions and propose DNMOT, an end-to-end trainable DeNoising
Transformer for MOT. To address the challenge of occlusions, we explicitly
simulate the scenarios when occlusions occur. Specifically, we augment the
trajectory with noises during training and make our model learn the denoising
process in an encoder-decoder architecture, so that our model can exhibit
strong robustness and perform well under crowded scenes. Additionally, we
propose a Cascaded Mask strategy to better coordinate the interaction between
different types of queries in the decoder to prevent the mutual suppression
between neighboring trajectories under crowded scenes. Notably, the proposed
method requires no additional modules like matching strategy and motion state
estimation in inference. We conduct extensive experiments on the MOT17, MOT20,
and DanceTrack datasets, and the experimental results show that our method
outperforms previous state-of-the-art methods by a clear margin.

本研究分析了传统的卷积神经网络方法和基于 Transformer 的方法在处理遮挡时的局限性，并提出了一种用于多目标跟踪的端到端可训练的去噪 Transformer 模型（DNMOT），通过在训练过程中显式地模拟遮挡情况来解决遮挡带来的挑战，实现了噪声去除的学习过程，从而使得模型在拥挤场景下表现出强大的鲁棒性和良好的性能。此外，我们提出了一种级联遮罩策略，以更好地协调解码器中不同类型查询之间的交互，以防止在拥挤场景下相邻轨迹之间的相互抑制，而且该方法在推理中不需要额外的匹配策略和运动状态估计模块。我们在 MOT17、MOT20 和 DanceTrack 数据集上进行了大量实验证明，我们的方法在性能上明显优于之前的最先进方法。