A Novel Real-Time Pedestrian Tracking Model Based on S-YOFEO in Complex Traffic Scenarios

A Novel Real-Time Pedestrian Tracking Model Based on S-YOFEO in Complex Traffic Scenarios | 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 A Novel Real-Time Pedestrian Tracking Model Based on S-YOFEO in Complex Traffic Scenarios Wenshun Sheng, Jiahui Shen, Qiming Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4586443/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 A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed with the aim of addressing the issue of target ID transformation and loss caused by the increase of practical background complexity. For the purpose of further enhancing the representation of small-scale features, a small target detection head is first introduced to the detection layer of YOLOv8 in this paper with the aim of collecting more detailed information by increasing the detection resolution of YOLOv8. Secondly, the Omni-Scale Network (OSNet) feature extraction network is implemented to enable accurate and efficient fusion of the extracted complex and comparable feature information, taking into account the restricted computational power of DeepSORT's original feature extraction network. Again, a novel adaptive forgetting Kalman filter algorithm (FSA) is devised to enhance the precision of model prediction and the effectiveness of parameter updates to adjust to the uncertain movement speed and trajectory of pedestrians in real scenarios. Following that, an accurate and stable association matching process is obtained by substituting Efficient-Intersection over Union (EIOU) for Complete-Intersection over Union (CIOU) in DeepSORT to boost the convergence speed and matching effect during association matching. Last but not least, One-Shot Aggregation (OSA) is presented as the trajectory feature extractor to deal with the various noise interferences in the complex scene. OSA is highly sensitive to information of different scales, and its one-time aggregation property substantially decreases the computational overhead of the model. According to the trial results, S-YOFEO has made some developments as its precision can reach 78.2% and its speed can reach 56.0 frames per second (FPS). Pedestrian tracking YOLOv8 DeepSORT association matching 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-4586443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321503617,"identity":"370fa603-19bb-43b8-b475-5032838da12b","order_by":0,"name":"Wenshun Sheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYHACxgcMPAzMbECWBLFamA1I1sIGV0mcFvkZ6c+qC2TusPMxMB+8zcNgl0dQi8GNHLPbM3ieAR3GlmzNw5BcTFiLdA7bbR6ew0AtPGbSPAwHEhsIOmx2+rNiiBb+b8RpYbidYMYMtYWNOC0G998YS/OA/MLMZmw5xyCZCIf1HH/4mbfnTrJ8e/PDG28q7IhwGAgw9hxIZmAGW0qUehD4ccCOaLWjYBSMglEw8gAAfmowqn5WtFMAAAAASUVORK5CYII=","orcid":"","institution":"Pujiang Institute, Nanjing Tech University","correspondingAuthor":true,"prefix":"","firstName":"Wenshun","middleName":"","lastName":"Sheng","suffix":""},{"id":321503619,"identity":"d0307a11-ce73-43ef-bde4-4dd269a9819b","order_by":1,"name":"Jiahui Shen","email":"","orcid":"","institution":"Pujiang Institute, Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Shen","suffix":""},{"id":321503620,"identity":"54c6a679-ae0d-4ca4-b35c-6478f875524d","order_by":2,"name":"Qiming Huang","email":"","orcid":"","institution":"Pujiang Institute, Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Qiming","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-06-15 12:06:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4586443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4586443/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60020229,"identity":"b6eedbe1-16cf-440b-9c76-0f15c51fe8e9","added_by":"auto","created_at":"2024-07-10 15:39:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6330516,"visible":true,"origin":"","legend":"","description":"","filename":"SuperComputing20240615.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4586443/v1_covered_c27d9474-ea5f-44f6-acfa-afed9e2cd25d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Real-Time Pedestrian Tracking Model Based on S-YOFEO in Complex Traffic Scenarios","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":"Pedestrian tracking, YOLOv8, DeepSORT, association matching","lastPublishedDoi":"10.21203/rs.3.rs-4586443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4586443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"A real-time stable multi-target tracking method based on the enhanced You Only Look Once-v8 (YOLOv8) and the optimized Simple Online and Realtime Tracking with a Deep association metric (DeepSORT) for real-time stable multi-target tracking (S-YOFEO) is proposed with the aim of addressing the issue of target ID transformation and loss caused by the increase of practical background complexity. 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