Image processing‐based Athletes' injury prevention mechanism

Abstract

AbstractThis study proposes a compressed domain video action recognition to prevent athlete injuries. The main idea is to use the motion vector and residual of the compressed video to construct new spatiotemporal features while extracting compressed code stream information. The new spatiotemporal features have the spatiotemporal relationship between motion vectors and residuals and the characteristic of precise object edges. Through verification on mainstream action recognition datasets (HMDB‐51, UCF‐101), the proposed method has a more negligible computational overhead than action recognition based on the traditional pixel domain and higher recognition accuracy than action recognition based on the video compression domain. Experiments have shown that the new spatiotemporal features based on the compressed domain have advantages such as solid spatiotemporal relationships and high information density, which can make action recognition results with high accuracy, detect abnormalities or patterns associated with injuries, and analyze an athlete's movements and provide personalized training recommendations.