@inproceedings{1a5a24117dae4aee83b7d1563054fd63,
title = "3D Human Pose Estimation for Martial Arts Analysis through Graph Convolutional Networks",
abstract = "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.",
keywords = "dataset unification, graph convolutional networks, martial arts, pose estimation, sports performance analysis",
author = "Giron, {Victor H.} and Chau, {Juan M.} and Anali Alfaro and Villota, {Elizabeth R.}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 14th International Conference on Machine Vision, ICMV 2021 ; Conference date: 08-11-2021 Through 12-11-2021",
year = "2022",
doi = "10.1117/12.2623512",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Wolfgang Osten and Dmitry Nikolaev and Jianhong Zhou",
booktitle = "Fourteenth International Conference on Machine Vision, ICMV 2021",
}