Towards Underwater Camouflaged Object Tracking: Benchmark and Baselines

• URL: http://arxiv.org/abs/2409.16902v2
• Date: Mon, 20 Jan 2025 13:01:46 GMT
• Title: Towards Underwater Camouflaged Object Tracking: Benchmark and Baselines
• Authors: Chunhui Zhang, Li Liu, Guanjie Huang, Hao Wen, Xi Zhou, Yanfeng Wang,
• Abstract summary: We propose the first large-scale multimodal underwater camouflaged object tracking dataset, namely UW-COT220. This paper first evaluates current advanced visual object tracking methods and SAM- and SAM2-based trackers in challenging underwater environments. We propose a novel vision-language tracking framework called VL-SAM2, based on the video foundation model SAM2.
• Score: 41.627959017482155
• License:
• Abstract: Over the past decade, significant progress has been made in visual object tracking, largely due to the availability of large-scale datasets. However, existing tracking datasets are primarily focused on open-air scenarios, which greatly limits the development of object tracking in underwater environments. To bridge this gap, we take a step forward by proposing the first large-scale multimodal underwater camouflaged object tracking dataset, namely UW-COT220. Based on the proposed dataset, this paper first comprehensively evaluates current advanced visual object tracking methods and SAM- and SAM2-based trackers in challenging underwater environments. Our findings highlight the improvements of SAM2 over SAM, demonstrating its enhanced ability to handle the complexities of underwater camouflaged objects. Furthermore, we propose a novel vision-language tracking framework called VL-SAM2, based on the video foundation model SAM2. Experimental results demonstrate that our VL-SAM2 achieves state-of-the-art performance on the UW-COT220 dataset. The dataset and codes can be accessible at \color{magenta}{https://github.com/983632847/Awesome-Multimodal-Object-Tracking}.

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