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IJMLC 2020 Vol.10(2): 358-367 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.943

3-D Human Pose Estimation in Traditional Martial Art Videos

Van-Hung Le

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. le@mica.edu.vn).

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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).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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