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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In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.