Underwater visual tracking with a large scale dataset and image enhancement
Alawode, Basit Javed, Sajid Dharejo, Fayaz Ali Ummar, Mehnaz Mahmoud, Arif Khan, Fahad Shahbaz Matas, Jiri
Alawode, Basit
Javed, Sajid
Dharejo, Fayaz Ali
Ummar, Mehnaz
Mahmoud, Arif
Khan, Fahad Shahbaz
Matas, Jiri
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Department
Computer Vision
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Journal article
Date
2026
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English
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Abstract
This paper presents a new dataset and a 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. The 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, for 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 labeled 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 at: https://github.com/BasitAlawode/UVOT400.
Citation
B. Alawode et al., “Underwater visual tracking with a large scale dataset and image enhancement,” Neurocomputing, vol. 671, p. 132586, Mar. 2026, doi: 10.1016/J.NEUCOM.2025.132586.
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Neurocomputing
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Elsevier