Design of sports training information analysis system based on a multi-target visual model under sensor-scale spatial transformation

Abstract

In the contemporary realm of athletic training, integrating technology is a pivotal determinant for augmenting athlete performance and refining training outcomes. The amalgamation of multi-target visual modeling with sensor technology imparts an enriched stratum of sports training data. Subsequently, the sensor scale-space transformation accentuates the comprehensive apprehension of data across diverse scales and angles. Hence, within this manuscript, addressing the multi-target tracking intricacies during sports training and competition, we posit a framework that amalgamates the shortest path elucidated by the K shortest paths (KSP) methodology with the pose information emanating from the Alphapose network. This framework recognizes the athlete’s shortest path through a convolutional neural network and KSP, followed by the amalgamation of these divergent data sources. The fusion unfolds by incorporating the athlete’s pose information grounded in Alphapose, culminating in a comprehensive integration of the two data streams. Consequently, synthesizing alpha-derived athlete information precipitates the ultimate amalgamation of the two information streams. The accomplished fusion, premised on Alphapose, forms the bedrock for multi-target tracking, culminating in a feature-rich synthesis. Empirical results reveal that after integrating these information streams, the Multiple Object Tracking Accuracy (MOTA) index and Global Multiple Object Tracking Accuracy (GMOTA) index surpass those of the solitary information tracking methods, thereby furnishing a technical underpinning and a foundation for information fusion within prospective sports training analysis systems.

Keywords

• Multiple objects tracking
• Physical training
• KSP
• Feature fusion
• Alphapose