Visual recognition and performance prediction of athletes based on target tracking EIA algorithm

In the past, the research of target tracking was often to track problems in a static background, and the tracking scenes were often stable, and the targets were special. However, target tracking is often a tracking problem in the face of realistic complex scenes, and the target and scene are more complex. Therefore, the target tracking algorithm still faces many challenges in practical applications, especially in sports visual feature recognition. Based on the needs of sports feature recognition, this study combines the EIA algorithm to construct a feature recognition model. Moreover, for the shortcomings of the compressed sensing tracking algorithm that cannot accurately and comprehensively describe the target shape through a single target feature, the multi-feature adaptive fusion method is used to visualize the target appearance model, thus improving the accuracy of target tracking. In addition, this study design experiments to analyze the performance of the algorithm model. The research results show that the algorithm model of this study has certain recognition effects.