Sport Classification from Multi-Player Trajectories with Set-over-Time Aggregation

Sport Classification from Multi-Player Trajectories with Set-over-Time Aggregation | 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 Research Article Sport Classification from Multi-Player Trajectories with Set-over-Time Aggregation Sawet Somnugpong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9214484/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 Sport classification is commonly studied from RGB video, but multi-player trajectories also provide a compact representation of movement, spacing, and interaction patterns. This paper investigates sport classification in a trajectory-only setting and introduces a set-over-time formulation for multi-player inputs. Each clip is represented as a sequence of frames, where each frame contains an unordered set of visible player trajectories, and the method is evaluated on a four-sport classification task built from MultiSports actor tubes. The proposed model first encodes the player set in each frame with permutation-invariant aggregation and then performs temporal aggregation for clip-level prediction. This design preserves frame-level multi-player structure before temporal summarization. Experiments compare the proposed approach with three compact trajectory baselines: mean pooling, mean-plus-standard-deviation pooling, and GRU-based aggregation. The proposed model achieves the best validation macro-F1 of 0.8793 and test macro-F1 of 0.8614, outperforming the strongest baseline by 0.0386 test macro-F1. Ablation results further show that entity weighting, temporal variability modeling, time attention, and the joint use of position and motion cues all contribute to performance. Error analysis indicates that trajectory-only recognition is effective, but confusion remains among team sports with partially similar collective motion patterns. Overall, the results show that set-over-time aggregation is an effective approach for sport classification from multi-player trajectories. sport classification multi-player trajectories set-over-time aggregation permutation-invariant modeling 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. 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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-9214484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613431685,"identity":"b151ccaf-3e5c-4641-af9d-878db1873898","order_by":0,"name":"Sawet Somnugpong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBADOQk4UwKPMjg4wMBgDFfIQ6yWxBlEa5FvP2P4+ANDXfrM2b3PJBhq7BjspRvwazE4k2NscIDhcO5smeNmEgzHkhl4ZA4Q0MKQu03iAMOB3HkSaWwSDGwHgA5LIOCw/rfbfxwAOkwOrOUfEVoYbuRuA3qfOUEapIWxjQgtBjfef5Y4w3DYcOacY8wWiX3JPDw3CDosLfFDBUOdvMTtNsYbH77ZybHPIOQwEGD8xwCJdKBiHiLUwwBRkT4KRsEoGAUjEgAAfrI7Ss033TAAAAAASUVORK5CYII=","orcid":"","institution":"Kamphaeng Phet Rajabhat University","correspondingAuthor":true,"prefix":"","firstName":"Sawet","middleName":"","lastName":"Somnugpong","suffix":""}],"badges":[],"createdAt":"2026-03-24 16:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9214484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9214484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960470,"identity":"9adaaf1f-56bd-4fa4-86d4-85ec91187868","added_by":"auto","created_at":"2026-04-15 09:21:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":837310,"visible":true,"origin":"","legend":"","description":"","filename":"sportclassificationSIVPRevised26032026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9214484/v1_covered_aa134900-e561-4156-b101-8b57222908c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sport Classification from Multi-Player Trajectories with Set-over-Time Aggregation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"sport classification, multi-player trajectories, set-over-time aggregation, permutation-invariant modeling","lastPublishedDoi":"10.21203/rs.3.rs-9214484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9214484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Sport classification is commonly studied from RGB video, but multi-player trajectories also provide a compact representation of movement, spacing, and interaction patterns. 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