3D Human Pose Estimation for Martial Arts Analysis through Graph Convolutional Networks

Victor H. Giron, Juan M. Chau, Anali Alfaro, Elizabeth R. Villota

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper presents a human pose estimation method for martial arts video analysis using a Semantic Graph Convolutional Network (SemGCN) instead of an ordinary convolutional neural network (CNN). The inputs for the model are videos from the Human3.6M dataset, in addition to the ones from Martial Arts, Dancing and Sports (MADS) dataset. A data unification process is described so that MADS joints can be adapted to the Human3.6M base setting. The performance of the model when only uses Human3.6M for training is compared to training with both Human3.6M and MADS datasets, resulting in a lower mean per-joint position error (MPJPE) for the latter. Finally, performance indicators such as the vertical position of the center of mass, balance and stability, are calculated for the MADS sequences in order to provide insights regarding martial arts execution.

Idioma originalInglés
Título de la publicación alojadaFourteenth International Conference on Machine Vision, ICMV 2021
EditoresWolfgang Osten, Dmitry Nikolaev, Jianhong Zhou
EditorialSPIE
ISBN (versión digital)9781510650442
DOI
EstadoPublicada - 2022
Evento14th International Conference on Machine Vision, ICMV 2021 - Rome, Italia
Duración: 8 nov. 202112 nov. 2021

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen12084
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

Conferencia14th International Conference on Machine Vision, ICMV 2021
País/TerritorioItalia
CiudadRome
Período8/11/2112/11/21

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