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Abnormal water quality will increase the occlusion rate among fish schools, which causes difficulties in fish detection and tracking. In order to solve this problem, a multiple fish tracking algorithm for red snapper is proposed in this paper. In the detection stage, we use the Otsu adaptive segmentation algorithm to extract fish targets based on the background subtraction method, following which the fish tracking feature parameters can be obtained based on the fish geometric features. In the tracking stage, the Kalman filter is employed to first estimate the motion state, and then the cost function is constructed from the position of the fish body, target area, and the direction information. Finally, fish school tracking is realized by the interframe relationship matrix. We applied several tracking methods with various schemes to experimental videos of swimming fish schools in different environments. The experimental results show that the proposed tracking algorithm exhibits improved performance and robustness. INDEX TERMS Data association, feature detection, multi-object tracking, water quality monitoring.

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