  Fish tracking is a key technology for obtaining movement trajectories and
identifying abnormal behavior. However, it faces considerable challenges,
including occlusion, multi-scale tracking, and fish deformation. Notably,
extant reviews have focused more on behavioral analysis rather than providing a
comprehensive overview of computer vision-based fish tracking approaches. This
paper presents a comprehensive review of the advancements of fish tracking
technologies over the past seven years (2017-2023). It explores diverse fish
tracking techniques with an emphasis on fundamental localization and tracking
methods. Auxiliary plugins commonly integrated into fish tracking systems, such
as underwater image enhancement and re-identification, are also examined.
Additionally, this paper summarizes open-source datasets, evaluation metrics,
challenges, and applications in fish tracking research. Finally, a
comprehensive discussion offers insights and future directions for vision-based
fish tracking techniques. We hope that our work could provide a partial
reference in the development of fish tracking algorithms.
