Effective Hierarchical Data Association in Multi-Object Tracking Using an Enhanced Network Flow Framework

Effective Hierarchical Data Association in Multi-Object Tracking Using an Enhanced Network Flow Framework | 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 Effective Hierarchical Data Association in Multi-Object Tracking Using an Enhanced Network Flow Framework Junwen Zhang, Xiaolong Zhang, Ziqi Zhu, Chunhua Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4642863/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 In the realm of multi-object tracking, the SORT model is esteemed for its simplicity and efficiency, yet its tracking efficacy hinges significantly on detector performance. Randomly discarding low-threshold detections can result in critical misses and track fragmentation. Hence, it is often complemented by a hierarchical data association strategy centered on detection thresholds. Nonetheless, relying solely on threshold-based categorization may segregate detections and lead to redundant detections for the same target, thereby increasing the impact of redundancies on tracking and causing trajectory drift. To address this, this paper introduces a novel hierarchical data association framework based on network flow, integrating historical trajectory and domain information to efficiently group detections and redundancies. Additionally, an effective global topological structure graph is proposed to manage redundant detections. Experimental results demonstrate competitive performance on MOTChallenge, with a 1-2\% improvement in MOTA over the benchmark, particularly nearing 2% on MOT20. Hierarchical Data Association Trajectory Drift Redundant Detections Network Flow Framework MOTchallenge 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-4642863","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327957468,"identity":"c142c3f5-28bc-423c-81fa-907e98995056","order_by":0,"name":"Junwen Zhang","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junwen","middleName":"","lastName":"Zhang","suffix":""},{"id":327957469,"identity":"bd6f8c3a-83cc-49d5-89f1-22030d219336","order_by":1,"name":"Xiaolong Zhang","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Zhang","suffix":""},{"id":327957470,"identity":"e3c6468a-6a26-40c8-b029-030df79f8092","order_by":2,"name":"Ziqi Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Zhu","suffix":""},{"id":327957471,"identity":"e01f54c7-3f9d-440b-baf2-9d64b28aa8a7","order_by":3,"name":"Chunhua Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDCCAzxAogLK4SFeyxkgZiNJC2MbKVr4jp89/Jl33uHE+fMbGB+8bWOQNyekRfJMXoLhzG2HEzccY2A2nNvGYLizgYAWgxs8Bgkft91O3MDGwCbN28aQYHCACC0HEufcTpzfxsD+m1gthg0fG24nNhxjYGMmSovkmRxjxhnH/htvOJbYLDnnnIThBkJa+I6fMf7MU5MmO7/58MEPb8ps5AnaggQYG4CEBPHqR8EoGAWjYBTgBgAID0ICW82F2wAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-06-26 12:54:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4642863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4642863/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60942389,"identity":"b23dd1b0-d48c-44ca-8482-645130c32e92","added_by":"auto","created_at":"2024-07-23 21:46:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5095414,"visible":true,"origin":"","legend":"","description":"","filename":"templateotcm4.22high.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4642863/v1_covered_faac2747-8ca3-47aa-b1dd-e80af2e8251a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effective Hierarchical Data Association in Multi-Object Tracking Using an Enhanced Network Flow Framework","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":"Hierarchical Data Association, Trajectory Drift, Redundant Detections, Network Flow Framework, MOTchallenge","lastPublishedDoi":"10.21203/rs.3.rs-4642863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4642863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the realm of multi-object \u0026nbsp;tracking, the SORT model is esteemed for its simplicity and efficiency, yet its tracking efficacy hinges significantly on detector performance. Randomly discarding low-threshold detections can result in critical misses and track fragmentation. Hence, it is often complemented by a hierarchical data association strategy centered on detection thresholds. Nonetheless, relying solely on threshold-based categorization may segregate detections and lead to redundant detections for the same target, thereby increasing the impact of redundancies on tracking and causing trajectory drift. To address this, this paper introduces a novel hierarchical data association framework based on network flow, integrating historical trajectory and domain information to efficiently group detections and redundancies. Additionally, an effective global topological structure graph is proposed to manage redundant detections. 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