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

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

Authors: Yang, Cheng-Yen, Huang, Hsiang-Wei, Kim, Pyong-Kun, Kuo, Chien-Kai, Chang, Jui-Wei, Kim, Kwang-Ju, Huang, Chung-I, Hwang, Jenq-Neng

Institutions: University of Washington, Electronics and Telecommunications Research Institute, National Center for High-Performance Computing

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6979266375

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