Recent advances of target tracking applications in aquaculture with emphasis on fish.

• A survey of machine vision for fish tracking in aquaculture. • A taxonomy of the techniques and classify the papers based on the taxonomy. • An analysis of fish detection and tracking methods. • A review of the application of fish tracking technology in fish research. • Summarized the datasets that can be used for fish tracking. In aquaculture, Behavioral monitoring of fish contributes to scientific management and reduces the threat of loss from disease and stress. Fish tracking technology plays an important role in behavior monitoring. It can pay attention to the movement of fish at any time and discover various abnormal behaviors in time. As a non-invasive method, computer vision is a powerful tool for fish tracking.. Its tracking principle is to establish the relationship between fish positions in a continuous video sequence and get the complete movement trajectory of the fish. Nevertheless, computer vision modeling used for fish tracking is riddled with many challenges, such as fish deformation, frequent occlusion, scale change, etc. Around these difficult issues, many scholars have carried out the research. In this paper, we review the progress of tracking algorithms in fish research. Then, methods for fish tracking before deep learning are introduced. Further, a detailed discussion of fish tracking methods employing deep learning such as tracking-by-detection, deep features combined with correlation filtering methods, Siamese networks, etc. Furthermore, we summarize datasets that can be used as fish tracking and give evaluation metrics in target tracking algorithms. In addition, experimental data of several mainstream tracking algorithms on a public tracking dataset are given. Finally, we discuss the outstanding findings and look forward to the fish tracking method combined with Transformer, aiming to provide a reference for accelerating the promotion of smart fishery and precision farming. [ABSTRACT FROM AUTHOR]