Enhancing Multiple Object Tracking Accuracy via Quantum Annealing

Enhancing Multiple Object Tracking Accuracy via Quantum Annealing | 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Article Enhancing Multiple Object Tracking Accuracy via Quantum Annealing Yasuyuki Ihara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4204266/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Multiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. This study introduces a novel approach that leverages quantum annealing (QA) to expedite computation speed, while enhancing tracking accuracy through the ensembling of object tracking processes. A method to improve the matching integration process is also proposed. By utilizing the sequential nature of MOT, this study further augments the tracking method via reverse annealing (RA). Experimental validation confirms the maintenance of high accuracy with an annealing time of a mere 3 µ s per tracking process. The proposed method holds significant potential for real-time MOT applications, including traffic flow measurement for urban traffic light control, collision prediction for autonomous robots and vehicles, and management of products mass-produced in factories. Multiple object tracking Quantum annealing Reverse annealing Combinatorial optimization problem Image recognition Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 16 May, 2024 Submission checks completed at journal 03 Apr, 2024 First submitted to journal 02 Apr, 2024 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-4204266","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286903651,"identity":"d5396229-d89c-4076-91ad-45147a87b844","order_by":0,"name":"Yasuyuki Ihara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACNgkoQ4K9gWQtPAeItQauRSKBSB180s3PPvxg2JY4c+bbgw9+MNjJ6RJyIJvMMeOZPQy3jaWl85INexiSjc0IOZBNIsGYgYfhtpycdI6ZNAPDgcRthLWkf2b8w3CbR07yjPlvIrXkGDODbJGW4DFjJlZLMbMM0C+SPTlAbECEX+RnpG9mfMNwO3HG8TOGH35U2MkR1AIGjP9gLANilI+CUTAKRsEoIAgAzFY2nflqExQAAAAASUVORK5CYII=","orcid":"","institution":"NEC Solution Innovators, Ltd.","correspondingAuthor":true,"prefix":"","firstName":"Yasuyuki","middleName":"","lastName":"Ihara","suffix":""}],"badges":[],"createdAt":"2024-04-02 06:22:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4204266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4204266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54291749,"identity":"54c8b93d-a911-4254-b6a8-14c8549a9c2d","added_by":"auto","created_at":"2024-04-08 11:55:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311555,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptQMOTSpringersubmit22.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4204266/v1_covered_8e361fed-a27c-4151-9f78-b381f321459c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Multiple Object Tracking Accuracy via Quantum Annealing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"quantum-information-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qinp","sideBox":"Learn more about [Quantum Information Processing](http://link.springer.com/journal/11128)","snPcode":"11128","submissionUrl":"https://submission.nature.com/new-submission/11128/3","title":"Quantum Information Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multiple object tracking, Quantum annealing, Reverse annealing, Combinatorial optimization problem, Image recognition","lastPublishedDoi":"10.21203/rs.3.rs-4204266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4204266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMultiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. 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