Abstract—Preserving, maintaining and teaching traditional
martial arts are very important activities in social life. That
helps preserve national culture, exercise and self-defense for
practitioners. However, traditional martial arts have many
different postures and activities of the body and body parts are
diverse. The problem of estimating the actions of the human
body still has many challenges, such as accuracy, obscurity, etc.
In this paper, we survey several strong studies in the recently
years for 3-D human pose estimation. Statistical tables have
been compiled for years, typical results of these studies on the
Human 3.6m dataset have been summarized. We also present a
comparative study for 3-D human pose estimation based on the
method that uses a single image. This study based on the
methods that use the Convolutional Neural Network (CNN) for
2-D pose estimation, and then using 3-D pose library for
mapping the 2-D results into the 3-D space. The CNNs model is
trained on the benchmark datasets as COCO dataset, Human
3.6M, MPII dataset, LSP, etc. From this comparative study, we
can see when there are good 2-D human pose estimation results,
then there will be good 3-D human pose estimation results.
Quantitative results are presented and evaluated.
Index Terms—2-D key points estimation, 3-D key points estimation, 3-D human pose estimation, convolutional neural network (CNN).
The author is with Tan Trao university, Vietnam (e-mail: Vanhung. firstname.lastname@example.org).
Cite: Van-Hung Le, "3-D Human Pose Estimation in Traditional Martial Art Videos," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 358-367, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).