Automated Human Movement Segmentation by Means of Human Pose Estimation in RGB-D Videos for Climbing Motion Analysis

Raul Beltrán B., Julia Richter, Ulrich Heinkel

2022

Abstract

The individual movement characterization of the human body parts is a fundamental task for the study of different activities executed by a person. Changes in position, speed and frequency of the different limbs reveal the kind of activity and allow us to estimate whether an action is well performed or not. Part of this characterization consists of establishing when the action begins and ends, but it is a difficult process when attempted by purely optical means since the subject’s pose in the image must first be extracted before proceeding with the movement variables identification. Human motion analysis has been approached in multiple studies through methods ranging from stochastic to artificial intelligence prediction, and more recently the latest research has been extended to the sport climbing employing the centre-of-mass analysis. In this paper, we present a method to identify the beginning and end of the movements of human body parts, through the analysis of kinematic variables obtained from RGB-D videos, with the aim of motion analysis in climbing. Application tests with OpenPose, PoseNet and Vision are presented to determine the optimal framework for human pose estimation in this sports scenario, and finally, the proposed method is validated to segment the movements of a climber on the climbing wall.

Paper Citation

in Harvard Style

Beltrán B. R., Richter J. and Heinkel U. (2022). Automated Human Movement Segmentation by Means of Human Pose Estimation in RGB-D Videos for Climbing Motion Analysis. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 366-373. DOI: 10.5220/0010817300003124

in Bibtex Style

@conference{visapp22,
author={Raul Beltrán B. and Julia Richter and Ulrich Heinkel},
title={Automated Human Movement Segmentation by Means of Human Pose Estimation in RGB-D Videos for Climbing Motion Analysis},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={366-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010817300003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Automated Human Movement Segmentation by Means of Human Pose Estimation in RGB-D Videos for Climbing Motion Analysis
SN - 978-989-758-555-5
AU - Beltrán B. R.
AU - Richter J.
AU - Heinkel U.
PY - 2022
SP - 366
EP - 373
DO - 10.5220/0010817300003124
PB - SciTePress