  Underwater object tracking (UOT) is a foundational task for identifying and
tracing submerged entities in underwater video sequences. However, current UOT
datasets suffer from limitations in scale, diversity of target categories and
scenarios covered, hindering the training and evaluation of modern tracking
algorithms. To bridge this gap, we take the first step and introduce WebUOT-1M,
\ie, the largest public UOT benchmark to date, sourced from complex and
realistic underwater environments. It comprises 1.1 million frames across 1,500
video clips filtered from 408 target categories, largely surpassing previous
UOT datasets, \eg, UVOT400. Through meticulous manual annotation and
verification, we provide high-quality bounding boxes for underwater targets.
Additionally, WebUOT-1M includes language prompts for video sequences,
expanding its application areas, \eg, underwater vision-language tracking. Most
existing trackers are tailored for open-air environments, leading to
performance degradation when applied to UOT due to domain gaps. Retraining and
fine-tuning these trackers are challenging due to sample imbalances and limited
real-world underwater datasets. To tackle these challenges, we propose a novel
omni-knowledge distillation framework based on WebUOT-1M, incorporating various
strategies to guide the learning of the student Transformer. To the best of our
knowledge, this framework is the first to effectively transfer open-air domain
knowledge to the UOT model through knowledge distillation, as demonstrated by
results on both existing UOT datasets and the newly proposed WebUOT-1M.
Furthermore, we comprehensively evaluate WebUOT-1M using 30 deep trackers,
showcasing its value as a benchmark for UOT research by presenting new
challenges and opportunities for future studies. The complete dataset, codes
and tracking results, will be made publicly available.
