Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910964877623296 |
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| author | Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Pyong-Kun Kuo, Chien-Kai Chang, Jui-Wei Kim, Kwang-Ju Huang, Chung-I Hwang, Jenq-Neng |
| author_facet | Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Pyong-Kun Kuo, Chien-Kai Chang, Jui-Wei Kim, Kwang-Ju Huang, Chung-I Hwang, Jenq-Neng |
| contents | We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task. Our method leverages the powerful pre-trained capabilities of SAM2 and incorporates several key techniques to enhance its performance in VOT applications. By combining SAM2 with our proposed optimizations, we achieved a first place AUC score of 89.4 on the 2024 ICPR Multi-modal Object Tracking challenge, demonstrating the effectiveness of our approach. This paper details our methodology, the specific enhancements made to SAM2, and a comprehensive analysis of our results in the context of VOT solutions along with the multi-modality aspect of the dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18111 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking Yang, Cheng-Yen Huang, Hsiang-Wei Kim, Pyong-Kun Kuo, Chien-Kai Chang, Jui-Wei Kim, Kwang-Ju Huang, Chung-I Hwang, Jenq-Neng Computer Vision and Pattern Recognition We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task. Our method leverages the powerful pre-trained capabilities of SAM2 and incorporates several key techniques to enhance its performance in VOT applications. By combining SAM2 with our proposed optimizations, we achieved a first place AUC score of 89.4 on the 2024 ICPR Multi-modal Object Tracking challenge, demonstrating the effectiveness of our approach. This paper details our methodology, the specific enhancements made to SAM2, and a comprehensive analysis of our results in the context of VOT solutions along with the multi-modality aspect of the dataset. |
| title | Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18111 |