摘要：For the last few years, the application of Siamese network in athletes’ three-dimensional motion tracking has greatly improved the efficiency of sports training. However, the accuracy of the Siamese network tracking algorithm is limited to a large extent. To solve the above problems, based on the channel attention mechanism, the key feature information perception module is innovatively proposed to promote the discriminant ability of the network model and make the network focus on the convolution feature changes of the target. On this basis, an online adaptive mask strategy is proposed, which adapts the subsequent frames according to the output state of the cross-correlation layer learned online to highlight the foreground object. Compared with other algorithms in annotated data set and MOT17 data set, this algorithm has more stable initial tracking performance, significantly improved accuracy compared with the benchmark, and high robustness tracking effect in complex scenes.
