Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910901704065024 |
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| author | Meng, Ting Fu, Chunyun Yan, Xiangyan Liang, Zheng Ji, Pan Wang, Jianwen Huang, Tao |
| author_facet | Meng, Ting Fu, Chunyun Yan, Xiangyan Liang, Zheng Ji, Pan Wang, Jianwen Huang, Tao |
| contents | Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object tracker that incorporates three key enhancements to improve the tracking accuracy and robustness. First, an adaptive Kalman filter is developed to dynamically update the covariance of measurement noise based on detection confidence and trajectory disappearance. Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors. Third, a dynamic appearance feature updating strategy is introduced, adjusting the relative weighting of historical and current appearance features based on appearance clarity and localization accuracy. Comprehensive evaluations on the MOT17 and MOT20 datasets demonstrate that the proposed Deep LG-Track consistently outperforms state-of-the-art trackers across multiple performance metrics, highlighting its effectiveness in multi-object tracking tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker Meng, Ting Fu, Chunyun Yan, Xiangyan Liang, Zheng Ji, Pan Wang, Jianwen Huang, Tao Computer Vision and Pattern Recognition Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object tracker that incorporates three key enhancements to improve the tracking accuracy and robustness. First, an adaptive Kalman filter is developed to dynamically update the covariance of measurement noise based on detection confidence and trajectory disappearance. Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors. Third, a dynamic appearance feature updating strategy is introduced, adjusting the relative weighting of historical and current appearance features based on appearance clarity and localization accuracy. Comprehensive evaluations on the MOT17 and MOT20 datasets demonstrate that the proposed Deep LG-Track consistently outperforms state-of-the-art trackers across multiple performance metrics, highlighting its effectiveness in multi-object tracking tasks. |
| title | Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.01457 |