Commercial Application of Biotechnology in Football Passing Motion Recognition

Hang Cao
School of Sports Medicine, Wuhan Sports University, Wuhan 430079, Hubei, China

DOI:https://doi.org/10.5912/jcb2480

Abstract:

The integration of biotechnology into sports analytics is unlocking new possibilities for the commercial application of fine-grained football action recognition.  This study explores a novel approach that combines biotechnology with deep learning to analyze and classify football passing actions.  By leveraging biomechanical data, such as player movement patterns and joint trajectory analysis, alongside video-based recognition techniques, we enhance the precision of fine-grained action classification.  A dedicated fine-grained football video dataset, Football, is introduced, encompassing live-action footage from multiple matches.  The dataset categorizes three broad action types—dribbling, passing, and shooting—and further refines these into 26 specific movements, with a particular emphasis on passing actions.  The dual-stream network proposed in this paper integrates biomechanical insights with temporal video features, offering improved recognition accuracy.  Comparative experiments conducted on the Football dataset and the MPII Cooking dataset demonstrate the method's superiority over mainstream approaches.  This research highlights the transformative potential of biotechnology-driven solutions in advancing the commercial applications of sports analytics, providing actionable insights for player performance optimization and tactical analysis.