Fish Tracking Based on Improved TLD Algorithm in Real-World Underwater Environment

Fish tracking in the natural underwater environment is of great value for many applications, such as fish behavior analysis and the estimation of fish population density. Due to the variety of swimming postures, uneven illumination, and complicated background in the real-world underwater environment, most existing object tracking algorithms are not ideal for fish tracking. In this paper, a tracking algorithm based on TLD (Tracking-Learning-Detection) is proposed, in which the shape and color features of fish are fully utilized to achieve accurate and rapid tracking. The proposed algorithm mainly focuses on the tracking module and detection module in the TLD framework. In the tracking module, a scale adaptive mean-shift algorithm is employed to deal with the variety of swimming postures and the uneven illumination by estimating the scale and using the color histogram of the background and target area. In the detection module, the Hu invariant moment of fish, which encodes the shape information, is used to detect the target quickly. The experiment results show that the proposed algorithm is effective and accurate for fish tracking in the real-world underwater environment.