This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained unexplored due to data inaccessibility. It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. Performance of traditional tracking methods designed primarily for terrestrial or open-air scenarios drops in such conditions. We address the problem by proposing a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers. To develop robust and accurate UVOT methods, large-scale datasets are required. To this end, we introduce a large-scale UVOT benchmark dataset consisting of 400 video segments and 275,000 manually annotated frames enabling underwater training and evaluation of deep trackers. The videos are labelled with several underwater-specific tracking attributes including watercolor variation, target distractors, camouflage, target relative size, and low visibility conditions. The UVOT400 dataset, tracking results, and the code are publicly available on: https://github.com/BasitAlawode/UWVOT400.

翻译：本文提出了一种用于水下视觉目标跟踪（UVOT）的新数据集及通用跟踪器增强方法。尽管水下跟踪具有重要意义，但由于数据获取困难，该领域至今尚未得到充分探索。水下环境呈现出非均匀光照条件、低能见度、缺乏清晰度、低对比度、伪装以及悬浮颗粒反射等独特挑战。主要针对陆地或户外场景设计的传统跟踪方法在此类条件下性能显著下降。为此，我们通过提出一种专门用于提升跟踪质量的新型水下图像增强算法来解决这一问题。该方法使现有的最先进（SOTA）视觉跟踪器的性能获得了显著提升，AUC指标最高提高了5.0%。要开发鲁棒且精确的UVOT方法，需要大规模数据集。为此，我们引入了一个大规模UVOT基准数据集，包含400个视频片段和275,000个手动标注帧，可用于深度跟踪器的水下训练与评估。视频标注了多种水下特定跟踪属性，包括水色变化、目标干扰物、伪装、目标相对尺寸及低能见度条件。UVOT400数据集、跟踪结果及代码已公开于：https://github.com/BasitAlawode/UWVOT400。