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.

翻译：鱼类追踪是获取运动轨迹和识别异常行为的关键技术。然而，该领域面临遮挡、多尺度追踪以及鱼类形变等重大挑战。值得注意的是，现有综述更多聚焦于行为分析，而非全面概述基于计算机视觉的鱼类追踪方法。本文系统梳理了过去七年（2017-2023年）鱼类追踪技术的演进历程，重点探讨了以基础定位与追踪方法为核心的多样化技术路线。研究还剖析了鱼类追踪系统中常见的辅助模块（如水下图像增强与重识别技术），同时归纳了开源数据集、评估指标、现存挑战及应用场景。最终通过综合讨论，为基于视觉的鱼类追踪技术提供洞见与未来发展方向。我们期望本工作能为鱼类追踪算法的研发提供部分参考依据。