Over the past decade, significant progress has been made in visual object tracking, largely due to the availability of large-scale training datasets. However, existing tracking datasets are primarily focused on open-air scenarios, which greatly limits the development of object tracking in underwater environments. To address this issue, we take a step forward by proposing the first large-scale underwater camouflaged object tracking dataset, namely UW-COT. Based on the proposed dataset, this paper presents an experimental evaluation of several advanced visual object tracking methods and the latest advancements in image and video segmentation. Specifically, we compare the performance of the Segment Anything Model (SAM) and its updated version, SAM 2, in challenging underwater environments. Our findings highlight the improvements in SAM 2 over SAM, demonstrating its enhanced capability to handle the complexities of underwater camouflaged objects. Compared to current advanced visual object tracking methods, the latest video segmentation foundation model SAM 2 also exhibits significant advantages, providing valuable insights into the development of more effective tracking technologies for underwater scenarios. The dataset will be accessible at \color{magenta}{https://github.com/983632847/Awesome-Multimodal-Object-Tracking}.

本研究解决了现有追踪数据集主要集中在开放空气场景下的问题，首次提出了大型水下伪装物体追踪数据集UW-COT。通过对比Segment Anything Model (SAM)及其升级版SAM 2在复杂水下环境中的表现，研究发现SAM 2在处理水下伪装物体方面显著优于SAM，为水下追踪技术的发展提供了重要依据。

面向水下伪装物体追踪的研究：SAM与SAM 2的实验评估