Deep Learning for Scene Understanding in Sports: Enhancing Game Analysis through Cultural Heritage Insights

Deep Learning for Scene Understanding in Sports: Enhancing Game Analysis through Cultural Heritage Insights | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep Learning for Scene Understanding in Sports: Enhancing Game Analysis through Cultural Heritage Insights Geming Zhu, Hang Zhao, Nannan Sun, Junhua Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7615771/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Scene understanding in sports analytics has witnessed significant advancements with the integration of deep learningtechniques. As sports continue to be a crucial element of cultural heritage, analyzing game dynamics through computationalmethods provides valuable insights into team strategies, player interactions, and tactical formations. Traditional sports analyticsapproaches heavily rely on handcrafted features and predefined heuristics, which often fail to generalize across diversegame scenarios and cultural variations in play styles. In this work, we propose a novel deep learning framework that fusesvisual scene understanding with cultural heritage metadata, offering a more contextualized interpretation of sports footage.Our approach leverages multimodal learning and self-supervised techniques to reduce dependency on extensive labeleddatasets, enabling scalability across modern and historical sports domains. Furthermore, we introduce a knowledge-guidedlearning mechanism that aligns deep learning models with domain-specific sports history, facilitating better recognition oftactical evolutions, historical play patterns, and culturally embedded strategies. Experimental evaluations on benchmarksports datasets demonstrate that our model outperforms existing state-of-the-art methods in both accuracy and contextualunderstanding. Beyond performance gains, our framework provides a holistic perspective that bridges modern analytics withthe preservation of sports heritage, enabling more comprehensive studies of the evolution of sports tactics, cultural influences,and strategic developments across eras. Ultimately, this work contributes to both advancing artificial intelligence applications insports and enriching historical sports research. Humanities/Cultural and media studies Social science/Cultural and media studies Physical sciences/Mathematics and computing Scene Understanding Sports Analytics Cultural Heritage Deep Learning Multimodal Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7615771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529867886,"identity":"bab54364-db63-4c4b-a730-e3b3cbd3fc45","order_by":0,"name":"Geming Zhu","email":"","orcid":"","institution":"Renwu Hall Martial Arts and Combat Club","correspondingAuthor":false,"prefix":"","firstName":"Geming","middleName":"","lastName":"Zhu","suffix":""},{"id":529867887,"identity":"47f50fd5-aacb-4d78-9863-2d00ce66c0e6","order_by":1,"name":"Hang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACA2YQeQCKPzaAeIyNB4jWwjizgUECSDXg18KApIWZF6wFwscJzNmZHz6uOGMjx3cj+eFt2x02dbrth4G21NhE49Ji2cxmbHjmRpqxJBBb555JkzA7kwjUciwttwGXww7zsEk2fDicuOFGgpl0btthCbMDQC2MDYfxaWH/2fDhf/2GG+nfpC1BWs4/JKiFjbHhxoEEgxs5ZtKMIC03CNgC8otkw5lkw5ln3hRb9ralSW67AbQlAY9fzPkPP/zYcMxOnu94+sYbP9ts+M3Opz988KHGBqcWBBBIgMQJGCQQVA4C/AeQtIyCUTAKRsEoQAIAO1poiOIwhY4AAAAASUVORK5CYII=","orcid":"","institution":"Jilin Sports University","correspondingAuthor":true,"prefix":"","firstName":"Hang","middleName":"","lastName":"Zhao","suffix":""},{"id":529867888,"identity":"87a64f29-184e-4961-bf84-508cad4fc3bb","order_by":2,"name":"Nannan Sun","email":"","orcid":"","institution":"Suzhou University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Sun","suffix":""},{"id":529867889,"identity":"db9a05ae-fef9-49b7-893b-4eeebe819844","order_by":3,"name":"Junhua Li","email":"","orcid":"","institution":"China Women’s University","correspondingAuthor":false,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-09-15 03:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7615771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7615771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93641988,"identity":"8f03ef25-1459-4b80-9dd7-b58867e33e18","added_by":"auto","created_at":"2025-10-16 03:03:58","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6195,"visible":true,"origin":"","legend":"","description":"","filename":"b2d0b97c536846f2bc66d5ccb58c9bd5.json","url":"https://assets-eu.researchsquare.com/files/rs-7615771/v1/88e61bcb50953a1a975afee5.json"},{"id":98948630,"identity":"454192a7-3633-4520-9557-15ad2f3a23d3","added_by":"auto","created_at":"2025-12-24 12:54:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2409062,"visible":true,"origin":"","legend":"","description":"","filename":"SR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7615771/v1_covered_a701b500-113e-4b58-aa3b-8123eb6b96f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDeep Learning for Scene Understanding in Sports: Enhancing Game Analysis through Cultural Heritage Insights\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Scene Understanding, Sports Analytics, Cultural Heritage, Deep Learning, Multimodal Learning","lastPublishedDoi":"10.21203/rs.3.rs-7615771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7615771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Scene understanding in sports analytics has witnessed significant advancements with the integration of deep learningtechniques. 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