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Visual Information Features and Machine Learning for Wushu Arts Tracking
Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial ar...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360718/ https://www.ncbi.nlm.nih.gov/pubmed/34394895 http://dx.doi.org/10.1155/2021/6713062 |
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author | Li, Jing Zhou, Guangren |
author_facet | Li, Jing Zhou, Guangren |
author_sort | Li, Jing |
collection | PubMed |
description | Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts' body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method. |
format | Online Article Text |
id | pubmed-8360718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-83607182021-08-13 Visual Information Features and Machine Learning for Wushu Arts Tracking Li, Jing Zhou, Guangren J Healthc Eng Research Article Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts' body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method. Hindawi 2021-08-04 /pmc/articles/PMC8360718/ /pubmed/34394895 http://dx.doi.org/10.1155/2021/6713062 Text en Copyright © 2021 Jing Li and Guangren Zhou. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Research Article Li, Jing Zhou, Guangren Visual Information Features and Machine Learning for Wushu Arts Tracking |
title | Visual Information Features and Machine Learning for Wushu Arts Tracking |
title_full | Visual Information Features and Machine Learning for Wushu Arts Tracking |
title_fullStr | Visual Information Features and Machine Learning for Wushu Arts Tracking |
title_full_unstemmed | Visual Information Features and Machine Learning for Wushu Arts Tracking |
title_short | Visual Information Features and Machine Learning for Wushu Arts Tracking |
title_sort | visual information features and machine learning for wushu arts tracking |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8360718/ https://www.ncbi.nlm.nih.gov/pubmed/34394895 http://dx.doi.org/10.1155/2021/6713062 |
work_keys_str_mv | AT lijing visualinformationfeaturesandmachinelearningforwushuartstracking AT zhouguangren visualinformationfeaturesandmachinelearningforwushuartstracking |