Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement

TL;DR

This paper introduces a large-scale underwater tracking dataset and a specialized image enhancement algorithm that significantly improves the performance of visual trackers in challenging underwater environments.

Contribution

The paper presents a new underwater image enhancement method and a comprehensive large-scale dataset for training and evaluating underwater visual object trackers.

Findings

Up to 5.0% improvement in AUC for state-of-the-art trackers

Introduction of the UVOT400 benchmark dataset with 275,000 frames

Enhanced tracking robustness in low visibility and camouflage conditions

Abstract

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.