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

• URL: http://arxiv.org/abs/2306.05238v2
• Date: Mon, 20 Nov 2023 06:57:05 GMT
• Title: SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
• Authors: Zelin Liu, Xinggang Wang, Cheng Wang, Wenyu Liu, Xiang Bai
• Abstract summary: 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. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
• Score: 84.64121608109087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

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