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 https://github.com/hustvl/SparseTrack.

本文提出了一种新的多目标跟踪算法 SparseTrack，通过在密集场景中进行稀疏分解，估计目标相对深度，并采用深度级联匹配算法将一个密集目标集转换为多个稀疏目标集，从而提高数据关联的效率，最终在 MOT17 和 MOT20 基准上取得了与最先进方法相当的表现。