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

2025-07-02

AI Summary

This study addresses the challenge of markerless, non-intrusive stride length and instantaneous velocity estimation in track-and-field training. We propose a monocular video-based, pose-driven analytical method that integrates YOLO-Pose for keypoint detection, probabilistic Hough transform for lane-line feature extraction, and homography transformation to establish a robust pixel-to-metric spatial mapping—enabling frame-by-frame stride and velocity estimation without field markings or wearable sensors. Experiments on multi-segment race videos from three sprinters demonstrate an average stride length estimation error below 4.2%, significantly outperforming purely geometric or purely learning-based baselines. To our knowledge, this is the first work to synergistically combine probabilistic Hough transform with human pose estimation for athletic gait parameter estimation. The approach delivers a low-cost, highly deployable tool for personalized, real-time training assessment by coaches.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Estimating stride length in athletes without markers
• Using monocular vision and pose estimation
• Assessing speed transition from video sequences

Innovation

• Markerless stride estimation using monocular vision
• Pose detection with probabilistic hough transform
• Homography transformation for gait parameter measurement