GradsSORT: Research on Multi-Object Tracking Algorithm Based on Pseudo-Gradient
Year: 2025
Type: article
Abstract: To address the issue of low tracking accuracy for occluded targets in complex scenes, we propose a multi-object tracking algorithm (GradsSORT) based on pseudo-gradient. First, targets are categorized into different gradient layers according to their degree of occlusion. Then, a hierarchical association strategy is adopted during data matching, prioritizing the association of detection boxes in lower gradient layers. This approach helps remove occluders for occluded targets, making it more effective for tracking tasks in crowded occlusion scenarios. To verify the effectiveness of the proposed algorithm, we implemented improvements based on the HybridSORT tracker and conducted experiments on the public datasets MOT17 and MOT20. The experimental results show that on the MOT20 dataset, the overall tracking performance metrics—HOTA, tracking accuracy (MOTA), and tracking stability (IDF1)—reached 64.8 (+0.5), 76.1 (+0.6), and 79.6 (+1.6), respectively. On the MOT17 dataset, the three key reference metrics, HOTA, MOTA, and IDF1, also improved to 63.5, 78.7, and 78.9, respectively. Our algorithm provides an effective solution for tracking targets in crowded occlusion scenarios.
DOI: 10.54097/9mg4z136
Authors: Xiufen Fu, Haifeng Sang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408990927