Motion analysis and training optimization for track and field athletes based on machine vision

Abstract

Athletics is a highly technical and comprehensive sport that requires not only the technical ability of athletes but also strong physical fitness. The traditional training method mainly relies on the experience and observation of coaches, lacking objective and quantitative analysis. On this basis, this article proposed an EfficientPose model based on mobile neural networks and combined it with the RANSAC matching algorithm to achieve real-time analysis and optimization of the process of track and field sports. The significance of this research lies in its potential to enhance athletic performance through advanced motion analysis. The purpose of the study was to develop a method for real-time analysis and optimization of athlete movements. Firstly, motion data was collected through a camera during the movement process. Secondly, the EfficientPose model was used to locate key moving points in real-time. Targeted training plans were then proposed to improve and optimize movements. The research results indicated that the EfficientPose model could effectively analyze and optimize athlete movements during training.

Keywords

MobileNet network model RANSAC matching algorithm EfficientPose model track and field athletes motion improvement

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