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

arXiv: https://arxiv.org/abs/2507.03016 · HTML: https://mlsanity.com/paper/2507.03016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [EfficientPose: Efficient Human Pose Estimation with Neural Architecture Search](https://mlsanity.com/paper/2012.07086/) (2020)

## Abstract (arXiv)

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.