Multi-Object Tracking (MOT) remains a vital component of intelligent video
analysis, which aims to locate targets and maintain a consistent identity for
each target throughout a video sequence. Existing works usually learn a
discriminative feature representation, such as motion and appearance, to
associate the detections across frames, which are easily affected by mutual
occlusion and background clutter in practice. In this paper, we propose a
simple yet effective two-stage feature learning paradigm to jointly learn
single-shot and multi-shot features for different targets, so as to achieve
robust data association in the tracking process. For the detections without
being associated, we design a novel single-shot feature learning module to
extract discriminative features of each detection, which can efficiently
associate targets between adjacent frames. For the tracklets being lost several
frames, we design a novel multi-shot feature learning module to extract
discriminative features of each tracklet, which can accurately refind these
lost targets after a long period. Once equipped with a simple data association
logic, the resulting VisualTracker can perform robust MOT based on the
single-shot and multi-shot feature representations. Extensive experimental
results demonstrate that our method has achieved significant improvements on
MOT17 and MOT20 datasets while reaching state-of-the-art performance on
DanceTrack dataset.

本文提出了一种简单而有效的两阶段特征学习模型，以共同学习不同目标的单帧特征和多帧特征，从而在跟踪过程中实现鲁棒的数据关联。通过引入单帧特征学习模块和多帧特征学习模块，可以有效地关联相邻帧之间的目标和长时间丢失的目标。通过简单的数据关联逻辑，所提出的 VisualTracker 能够基于单帧特征和多帧特征进行鲁棒的多目标跟踪，实验结果表明该方法在 MOT17 和 MOT20 数据集上取得了显著的改进，并在 DanceTrack 数据集上达到了最先进的性能。