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：探索鲁棒且高效的关联方法一直是多目标跟踪（MOT）中的重要问题。尽管现有跟踪方法已取得令人瞩目的性能，但密集场景中的拥堵和频繁遮挡仍是多目标跟踪面临的挑战性问题。我们揭示出，对密集场景进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。其次，我们设计了深度级联匹配（DCM）算法，该算法可利用所获取的深度信息，将密集目标集合转换为多个稀疏目标子集，并按由近及远的顺序对这些稀疏目标子集进行数据关联。通过将伪深度方法与DCM策略集成到数据关联过程中，我们提出一种名为SparseTrack的新型跟踪器。SparseTrack为解决具有挑战性的密集场景MOT问题提供了新视角。仅使用IoU匹配，SparseTrack在MOT17和MOT20基准上即取得了与最先进（SOTA）方法相当的性能。代码和模型已在 \url{https://github.com/hustvl/SparseTrack} 公开。