Exploring robust and efficient association methods has always been an important issue in multiple-object tracking (MOT). Although existing tracking methods have achieved impressive performance, congestion and frequent occlusions still pose challenging problems in multi-object tracking. We reveal that performing sparse decomposition on dense scenes is a crucial step to enhance the performance of associating occluded targets. To this end, we propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images. Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets and perform data association on these sparse target subsets in order from near to far. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack. SparseTrack provides a new perspective for solving the challenging crowded scene MOT problem. Only using IoU matching, SparseTrack achieves comparable performance with the state-of-the-art (SOTA) methods on the MOT17 and MOT20 benchmarks. Code and models are publicly available at \url{https://github.com/hustvl/SparseTrack}.

翻译：探索鲁棒且高效的数据关联方法一直是多目标跟踪（MOT）领域的重要课题。尽管现有跟踪方法已取得显著性能，但遮挡与频繁的交互问题仍对多目标跟踪构成严峻挑战。本文揭示了在密集场景中执行稀疏分解是提升遮挡目标关联性能的关键步骤。为此，我们提出一种伪深度估计方法，用于从二维图像中获取目标的相对深度信息。其次，设计深度级联匹配（DCM）算法，利用深度信息将密集目标集分解为多个稀疏子集，并按由近至远的顺序对这些稀疏子集进行数据关联。通过将伪深度方法与DCM策略集成至数据关联流程，我们提出新型跟踪器SparseTrack，为破解拥挤场景下的MOT难题提供了新视角。仅采用IoU匹配，SparseTrack在MOT17和MOT20基准上即取得与最先进（SOTA）方法相媲美的性能。代码及模型已开源至 \url{https://github.com/hustvl/SparseTrack}。