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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.05172 |
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| _version_ | 1866916885096824832 |
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| author | Wu, Zewei Wang, Longhao Wang, Cui Teixeira, César Ke, Wei Xiong, Zhang |
| author_facet | Wu, Zewei Wang, Longhao Wang, Cui Teixeira, César Ke, Wei Xiong, Zhang |
| contents | Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering Wu, Zewei Wang, Longhao Wang, Cui Teixeira, César Ke, Wei Xiong, Zhang Computer Vision and Pattern Recognition Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework. |
| title | Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.05172 |