Application of multi-scale feature fusion algorithm based on motion wearable sensors in feature extraction of sports images

The utilization of moving image feature extraction in sports teaching has garnered increasing attention. However, traditional feature extraction algorithms often struggle to meet the diverse and complex demands of moving images. To address this challenge, this paper proposes a multi-scale feature fusion algorithm aimed at improving feature extraction in moving images. The algorithm begins by decomposing the moving image into multiple scales, followed by extracting features from each scale using a feature extraction network. To obtain a more comprehensive and accurate feature representation, feature fusion technology is employed to merge the features from different scales. The proposed algorithm, based on multi-scale feature fusion, exhibits a significant improvement in both accuracy and stability when compared to traditional feature extraction algorithms. Byaccurately extracting and representing the crucial features within moving images, the algorithm contributes to an improved understanding of athletes' movements, enabling instructors to provide more targeted and insightful feedback. This algorithm effectively captures key features within the moving images, providing robust support for tasks such as movement analysis and skill evaluation in sports teaching.