Markerless Stride Length estimation in Athletic using Pose Estimation with monocular vision

TL;DR

This paper presents a computer vision method using pose estimation and homography to accurately measure athletes' stride length and speed from video, aiding training and performance monitoring.

Contribution

It introduces a novel approach combining pose detection and homography for markerless stride length estimation in athletic videos.

Findings

Effective stride length estimation demonstrated on race videos.

Potential for real-time athlete performance monitoring.

Useful tool for coaching and training applications.

Abstract

Performance measures such as stride length in athletics and the pace of runners can be estimated using different tricks such as measuring the number of steps divided by the running length or helping with markers printed on the track. Monitoring individual performance is essential for supporting staff coaches in establishing a proper training schedule for each athlete. The aim of this paper is to investigate a computer vision-based approach for estimating stride length and speed transition from video sequences and assessing video analysis processing among athletes. Using some well-known image processing methodologies such as probabilistic hough transform combined with a human pose detection algorithm, we estimate the leg joint position of runners. In this way, applying a homography transformation, we can estimate the runner stride length. Experiments on various race videos with three different runners demonstrated that the proposed system represents a useful tool for coaching and training. This suggests its potential value in measuring and monitoring the gait parameters of athletes.