Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking

• URL: http://arxiv.org/abs/2505.18111v1
• Date: Fri, 23 May 2025 17:04:56 GMT
• Title: Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking
• Authors: Cheng-Yen Yang, Hsiang-Wei Huang, Pyong-Kun Kim, Chien-Kai Kuo, Jui-Wei Chang, Kwang-Ju Kim, Chung-I Huang, Jenq-Neng Hwang,
• Abstract summary: We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task.<n>Our method leverages the powerful pre-trained capabilities of SAM2 and incorporates several key techniques to enhance its performance in VOT applications.
• Score: 18.75180282940254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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