SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth

TL;DR

SparseTrack introduces a novel multi-object tracking approach that leverages pseudo-depth estimation and scene decomposition to improve association accuracy in crowded scenes, achieving competitive results with state-of-the-art methods.

Contribution

The paper presents a new pseudo-depth estimation method and depth cascading matching algorithm, enabling sparse scene decomposition for improved multi-object tracking.

Findings

Achieves comparable performance with SOTA on MOT17 and MOT20 benchmarks.

Effectively handles occlusions and congestion in crowded scenes.

Utilizes only IoU matching for data association.

Abstract

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}.