SparseTrack：基于伪深度进行场景分解的多目标跟踪
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
AI总结：
提出SparseTrack多目标跟踪器，通过伪深度估计和深度级联匹配算法将密集目标集分解为稀疏子集进行关联，有效解决拥挤场景下的遮挡问题。
摘要：
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。尽管现有的跟踪方法已取得令人印象深刻的性能，但拥挤和频繁遮挡仍是多目标跟踪中的难题。我们揭示了对密集场景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2D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，该算法利用获取的深度信息将密集目标集转化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。通过将伪深度方法和DCM策略整合到数据关联过程中，我们提出了一种名为SparseTrack的新跟踪器。SparseTrack为解决具有挑战性的拥挤场景MOT问题提供了新视角。仅使用IoU匹配，SparseTrack在MOT17和MOT20基准上取得了与现有技术（SOTA）方法相当的性能。代码和模型已在\url{https://github.com/hustvl/SparseTrack}公开。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
