Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916642249768960 |
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| author | Li, Kunjun Yang, Cheng-Yen Huang, Hsiang-Wei Hwang, Jenq-Neng |
| author_facet | Li, Kunjun Yang, Cheng-Yen Huang, Hsiang-Wei Hwang, Jenq-Neng |
| contents | This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI tracker with a person re-identification (Re-ID) module and advanced post-processing techniques to enhance accuracy in challenging skiing scenarios. We employ an OSNet-based Re-ID model to minimize identity switches and utilize YOLOv11 with Kalman filtering or STARK-based object detection for precise equipment tracking. When evaluated on the SkiTB dataset, ReID-SAM achieved a state-of-the-art F1-score of 0.870, surpassing existing methods across alpine, ski jumping, and freestyle skiing disciplines. These results demonstrate significant advancements in skier tracking accuracy and provide valuable insights for computer vision applications in winter sports. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025 Li, Kunjun Yang, Cheng-Yen Huang, Hsiang-Wei Hwang, Jenq-Neng Computer Vision and Pattern Recognition Image and Video Processing This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI tracker with a person re-identification (Re-ID) module and advanced post-processing techniques to enhance accuracy in challenging skiing scenarios. We employ an OSNet-based Re-ID model to minimize identity switches and utilize YOLOv11 with Kalman filtering or STARK-based object detection for precise equipment tracking. When evaluated on the SkiTB dataset, ReID-SAM achieved a state-of-the-art F1-score of 0.870, surpassing existing methods across alpine, ski jumping, and freestyle skiing disciplines. These results demonstrate significant advancements in skier tracking accuracy and provide valuable insights for computer vision applications in winter sports. |
| title | Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025 |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2503.01907 |