Using Computer Vision and Deep Learning Methods to Capture Skeleton Push Start Performance

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Abstract

This study aimed to employ computer vision and deep learning methods in order to capture
skeleton push start kinematics. Push start data were captured concurrently by a markerbased motion capture system and a custom markerless system. Very good levels of
agreement were found between systems, particularly for spatial based variables (step
length error 0.001 ± 0.012 m) while errors for temporal variables (ground contact time and
flight time) were within 1.5 frames of the criterion measures. The computer vision based
methods tested in this research provide a viable alternative to marker-based motion
capture systems. Furthermore they can be deployed into challenging, real world
environments to non-invasively capture data where traditional approaches would fail.
Original languageEnglish
Publication statusPublished - 2 Mar 2020
Event38th International Conference on Biomechanics in Sport - LJMU, Liverpool, UK United Kingdom
Duration: 21 Jul 202025 Jul 2020
https://www.isbs2020.org/home.html

Conference

Conference38th International Conference on Biomechanics in Sport
Abbreviated titleISBS 2020
CountryUK United Kingdom
CityLiverpool
Period21/07/2025/07/20
Internet address

Keywords

  • Winter sports, computer vision, deep learning, pose estimation.

Cite this

Needham, L., Evans, M., Cosker, D., & Colyer, S. (2020). Using Computer Vision and Deep Learning Methods to Capture Skeleton Push Start Performance. Abstract from 38th International Conference on Biomechanics in Sport, Liverpool, UK United Kingdom.