Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Jing, Zhou, Guangren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
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
_version_ 1783737799646642176
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