Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion
In the field of Multi-Object Tracking (MOT), the current mainstream approach is the tracking by detection paradigm, which heavily relies on the accuracy of the detector, the comprehensiveness of feature extraction, and the superiority of the data association matching algorithm. Most existing pedestr...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10908239/ |
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| author | Yihuai Zhu Zhandong Liu Ke Li Yong Li Xiangwei Qi Nan Ding |
| author_facet | Yihuai Zhu Zhandong Liu Ke Li Yong Li Xiangwei Qi Nan Ding |
| author_sort | Yihuai Zhu |
| collection | DOAJ |
| description | In the field of Multi-Object Tracking (MOT), the current mainstream approach is the tracking by detection paradigm, which heavily relies on the accuracy of the detector, the comprehensiveness of feature extraction, and the superiority of the data association matching algorithm. Most existing pedestrian re-identification methods are based on convolutional neural networks (CNNs), which struggle to balance both local and global features of pedestrians. Given that current detectors are already highly advanced, this paper proposes a full-scale feature fusion-based multi-object pedestrian tracking algorithm named BOS-SORT. The algorithm utilizes the proposed feature extraction network, Better Omni-Scale Net (BOSNet), to captures both global and local appearance information, effectively reducing appearance information loss. Furthermore, it employs an improved association matching algorithm, AveSort, to combines IoU and appearance features for initial data association while smoothing target motion states to minimize matching errors in high-similarity scenarios. The BOS-SORT system integrates these methods and demonstrates exceptional capability in aligning global trajectories with real trajectories. Experimental results show that it achieves state-of-the-art Higher Order Tracking Accuracy (HOTA) scores of 66.2 and 65.3 on the MOT17 and MOT20 datasets, respectively. |
| format | Article |
| id | doaj-art-c72fd93dbc224a12a9de504c07feb7a0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c72fd93dbc224a12a9de504c07feb7a02025-08-20T03:02:15ZengIEEEIEEE Access2169-35362025-01-0113391073911610.1109/ACCESS.2025.354681810908239Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature FusionYihuai Zhu0https://orcid.org/0009-0002-8728-7069Zhandong Liu1https://orcid.org/0000-0002-6073-4618Ke Li2Yong Li3Xiangwei Qi4Nan Ding5School of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaSchool of Computer Science and Technology, Xinjiang Normal University, Ürümqi, ChinaIn the field of Multi-Object Tracking (MOT), the current mainstream approach is the tracking by detection paradigm, which heavily relies on the accuracy of the detector, the comprehensiveness of feature extraction, and the superiority of the data association matching algorithm. Most existing pedestrian re-identification methods are based on convolutional neural networks (CNNs), which struggle to balance both local and global features of pedestrians. Given that current detectors are already highly advanced, this paper proposes a full-scale feature fusion-based multi-object pedestrian tracking algorithm named BOS-SORT. The algorithm utilizes the proposed feature extraction network, Better Omni-Scale Net (BOSNet), to captures both global and local appearance information, effectively reducing appearance information loss. Furthermore, it employs an improved association matching algorithm, AveSort, to combines IoU and appearance features for initial data association while smoothing target motion states to minimize matching errors in high-similarity scenarios. The BOS-SORT system integrates these methods and demonstrates exceptional capability in aligning global trajectories with real trajectories. Experimental results show that it achieves state-of-the-art Higher Order Tracking Accuracy (HOTA) scores of 66.2 and 65.3 on the MOT17 and MOT20 datasets, respectively.https://ieeexplore.ieee.org/document/10908239/Multi-object trackingobject detectionperson re-identificationpedestrian trackingdata association |
| spellingShingle | Yihuai Zhu Zhandong Liu Ke Li Yong Li Xiangwei Qi Nan Ding Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion IEEE Access Multi-object tracking object detection person re-identification pedestrian tracking data association |
| title | Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion |
| title_full | Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion |
| title_fullStr | Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion |
| title_full_unstemmed | Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion |
| title_short | Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion |
| title_sort | research on multi objective pedestrian tracking algorithm based on full size feature fusion |
| topic | Multi-object tracking object detection person re-identification pedestrian tracking data association |
| url | https://ieeexplore.ieee.org/document/10908239/ |
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