Sports Video Object Tracking Algorithm Based on Optimized Particle Filter | EAI Endorsed Transactions on Scalable Information Systems

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Sports Video Object Tracking Algorithm Based on Optimized Particle Filter | EAI Endorsed Transactions on Scalable Information Systems </title> <link rel="icon" href="https://publications.eai.eu/public/journals/8/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.3.0.18"> <meta name="og:site_name" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta name="og:type" content="article"/> <meta name="og:title" content="Sports Video Object Tracking Algorithm Based on Optimized Particle Filter"/> <meta name="og:description" content=" INTRODUCTION: Particle filter based human motion video target tracking technology has become a trend. This project intends to apply particle filters to image processing of human activities. Firstly, an improved particle filter model is used to track moving video objects. The purpose is to further improve the tracking effect and increase the tracking accuracy. HSV distribution model was used to establish target observation model. The algorithm is combined with the weight reduction algorithm to realize the human motion trajectory detection in the target observation mode. The model was then confirmed by an examination of sports player videos. Experiments show that this method can be used to track people in moving images of sports. Compared with other methods, this method has higher computational accuracy and speed. "/> <meta name="og:url" content="https://publications.eai.eu/index.php/sis/article/view/3935"/> <meta name="og:locale" content="en_US"/> <meta name="og:image" content=""/> <meta name="article:published_time" content="2023-10-23"/> <meta name="article:tag" content="Moving target detection"/> <meta name="article:tag" content="Gaussian mixture model"/> <meta name="article:tag" content="Particle filter"/> <meta name="article:tag" content="RGB color columnar structure"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Qingbao Wang"/> <meta name="DC.Creator.PersonalName" content="Chenbo Zhao"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-10-23"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-09-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-02-20"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-02-20"/> <meta name="DC.Description" xml:lang="en" content="INTRODUCTION: Particle filter based human motion video target tracking technology has become a trend. This project intends to apply particle filters to image processing of human activities. Firstly, an improved particle filter model is used to track moving video objects. The purpose is to further improve the tracking effect and increase the tracking accuracy. HSV distribution model was used to establish target observation model. The algorithm is combined with the weight reduction algorithm to realize the human motion trajectory detection in the target observation mode. The model was then confirmed by an examination of sports player videos. Experiments show that this method can be used to track people in moving images of sports. Compared with other methods, this method has higher computational accuracy and speed."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="3935"/> <meta name="DC.Identifier.DOI" content="10.4108/eetsis.3935"/> <meta name="DC.Identifier.URI" content="https://publications.eai.eu/index.php/sis/article/view/3935"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Qingbao Wang, Chenbo Zhao"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by-nc-sa/4.0"/> <meta name="DC.Source" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta name="DC.Source.ISSN" content="2032-9407"/> <meta name="DC.Source.Issue" content="3"/> <meta name="DC.Source.Volume" content="11"/> <meta name="DC.Source.URI" content="https://publications.eai.eu/index.php/sis"/> <meta name="DC.Subject" xml:lang="en" content="RGB color columnar structure"/> <meta name="DC.Title" content="Sports Video Object Tracking Algorithm Based on Optimized Particle Filter"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Research articles"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta name="citation_journal_abbrev" content="EAI Endorsed Scal Inf Syst"/> <meta name="citation_issn" content="2032-9407"/> <meta name="citation_author" content="Qingbao Wang"/> <meta name="citation_author_institution" content="BaiCheng Normal University "/> <meta name="citation_author" content="Chenbo Zhao"/> <meta name="citation_author_institution" content="Jiamusi University "/> <meta name="citation_title" content="Sports Video Object Tracking Algorithm Based on Optimized Particle Filter"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024"/> <meta name="citation_volume" content="11"/> <meta name="citation_issue" content="3"/> <meta name="citation_doi" content="10.4108/eetsis.3935"/> <meta name="citation_abstract_html_url" content="https://publications.eai.eu/index.php/sis/article/view/3935"/> <meta name="citation_keywords" xml:lang="en" content="Moving target detection"/> <meta name="citation_keywords" xml:lang="en" content="Gaussian mixture model"/> <meta name="citation_keywords" xml:lang="en" content="Particle filter"/> <meta name="citation_keywords" xml:lang="en" content="RGB color columnar structure"/> <meta name="citation_pdf_url" content="https://publications.eai.eu/index.php/sis/article/download/3935/2626"/> <meta name="citation_reference" content="Liu, Y., & Ji, Y. 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Systems </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research articles </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Sports Video Object Tracking Algorithm Based on Optimized Particle Filter </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Qingbao Wang </span> <span class="affiliation"> BaiCheng Normal University <a href="https://ror.org/01djkf495"><?xml version="1.0" encoding="UTF-8" standalone="no"?>  <svg xmlns:i="&ns_ai;" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:svg="http://www.w3.org/2000/svg" xmlns="http://www.w3.org/2000/svg" xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" 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<li> <span class="name"> Chenbo Zhao </span> <span class="affiliation"> Jiamusi University <a href="https://ror.org/01vasff55"><?xml version="1.0" encoding="UTF-8" standalone="no"?>  <svg xmlns:i="&ns_ai;" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:svg="http://www.w3.org/2000/svg" xmlns="http://www.w3.org/2000/svg" xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape" version="1.1" id="Layer_1" x="0px" y="0px" viewBox="0 0 23.4225 16.696501" xml:space="preserve" sodipodi:docname="rorId.svg" inkscape:version="0.92.5 (2060ec1f9f, 2020-04-08)" width="23.422501" height="16.696501"><metadata id="metadata93"><rdf:RDF><cc:Work rdf:about=""><dc:format>image/svg+xml</dc:format><dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage" /></cc:Work></rdf:RDF></metadata><defs id="defs91" /><sodipodi:namedview 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human motion video target tracking technology has become a trend. This project intends to apply particle filters to image processing of human activities. Firstly, an improved particle filter model is used to track moving video objects. The purpose is to further improve the tracking effect and increase the tracking accuracy. HSV distribution model was used to establish target observation model. The algorithm is combined with the weight reduction algorithm to realize the human motion trajectory detection in the target observation mode. The model was then confirmed by an examination of sports player videos. Experiments show that this method can be used to track people in moving images of sports. Compared with other methods, this method has higher computational accuracy and speed.</p> </section>  <a href="https://plu.mx/plum/a/?doi=10.4108/eetsis.3935" class="plumx-summary" data-hide-when-empty="true" data-orientation="vertical" ></a>  <section class="item references"> <h2 class="label"> References </h2> <div class="value"> <p>Liu, Y., & Ji, Y. Target recognition of sport athletes based on deep learning and convolutional neural network. Journal of Intelligent & Fuzzy Systems, 2021;40(2): 2253-2263. </p> <p>Abdelbaky, A., & Aly, S. Two-stream spatiotemporal feature fusion for human action recognition. The Visual Computer, 2021; 37(7): 1821-1835. </p> <p>Liu, N., & Liu, P. Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things. The Journal of Supercomputing, 2022; 78(1): 123-143. </p> <p>Cai, H. Application of intelligent real-time image processing in fitness motion detection under internet of things. The Journal of Supercomputing, 2022; 78(6): 7788-7804. </p> <p>Zhang, X. Application of human motion recognition utilizing deep learning and smart wearable device in sports. International Journal of System Assurance Engineering and Management, 2021;12(4): 835-843. </p> <p>Hao, Z., Wang, X., & Zheng, S. Recognition of basketball players’ action detection based on visual image and Harris corner extraction algorithm. Journal of Intelligent & Fuzzy Systems, 2021; 40(4): 7589-7599. </p> <p>Liu, A., Xie, H., & Ahmed, K. Fault detection technology of national traditional sports equipment based on optical microscope imaging technology. Alexandria Engineering Journal, 2021; 60(2): 2697-2705. </p> <p>Şah, M., & Direkoğlu, C. Review and evaluation of player detection methods in field sports: Comparing conventional and deep learning based methods. Multimedia Tools and Applications, 2023;82(9): 13141-13165. </p> <p>Papic, C., Sanders, R. H., Naemi, R., Elipot, M., & Andersen, J. Improving data acquisition speed and accuracy in sport using neural networks. Journal of Sports Sciences, 2021; 39(5): 513-522. </p> <p>Tsai, M. F., & Huang, S. H. Enhancing accuracy of human action Recognition System using Skeleton Point correction method. Multimedia Tools and Applications, 2022; 81(5): 7439-7459. </p> <p>Abdelbaky, A., & Aly, S. Human action recognition using three orthogonal planes with unsupervised deep convolutional neural network. Multimedia Tools and Applications, 2021; 80(13): 20019-20043. </p> <p>Gao, H., Xu, K., Cao, M., Xiao, J., Xu, Q., & Yin, Y. The deep features and attention mechanism-based method to dish healthcare under social iot systems: an empirical study with a hand-deep local–global net. IEEE Transactions on Computational Social Systems, 2021; 9(1): 336-347. </p> <p>Hao, L., & Pandey, H. M. Research on the Positioning Technology of Sports 3D Teaching Action Based on Machine Vision. Mobile Networks and Applications, 2022; 27(6): 2419-2428. </p> <p>Li, J., & Gu, D. Research on basketball players’ action recognition based on interactive system and machine learning. Journal of Intelligent & Fuzzy Systems, 2021; 40(2): 2029-2039. </p> <p>Meng, L., & Qiao, E. Analysis and design of dual-feature fusion neural network for sports injury estimation model. Neural Computing and Applications, 2023; 35(20): 14627-14639. </p> <p>Saqib, S., Ditta, A., Khan, M. A., Kazmi, S. A. R., & Alquhayz, H. Intelligent dynamic gesture recognition using CNN empowered by edit distance. Cmc-Computers Materials & Continua, 2021; 66(2): 2061-2076. </p> <p>Cai, W., Wei, Z., Liu, R., Zhuang, Y., Wang, Y., & Ning, X. Remote sensing image recognition based on multi-attention residual fusion networks. ASP Transactions on Pattern Recognition and Intelligent Systems, 2021; 1(1): 1-8. </p> </div> </section> </div> <div class="entry_details"> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://publications.eai.eu/index.php/sis/article/view/3935/2626"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>23-10-2023</span> </div> </section> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry"><div class="csl-left-margin">1.</div><div class="csl-right-inline">Wang Q, Zhao C. Sports Video Object Tracking Algorithm Based on Optimized Particle Filter. 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