A Survey of Fish Tracking Techniques Based on Computer Vision
It addresses the need for a focused overview of fish tracking methods for researchers in aquaculture or marine biology, but it is incremental as a survey paper.
This paper provides a comprehensive review of computer vision-based fish tracking techniques from 2017 to 2023, covering localization methods, auxiliary plugins, datasets, and challenges, aiming to serve as a reference for algorithm development.
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.