Deep Learning-based Multi-class Object Tracking With Occlusion Handling Mechanism in Uav Videos

Deep Learning-based Multi-class Object Tracking With Occlusion Handling Mechanism in Uav Videos | 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 Deep Learning-based Multi-class Object Tracking With Occlusion Handling Mechanism in Uav Videos A Ancy Micheal, A Annie Micheal, Anurekha Gopinathan, B U Anu Barathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4488926/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 Unmanned Aerial Vehicles (UAVs) play a crucial role in tracking-based applications, particularly in real-time situations such as rescue missions and surveillance. However, tracking objects with occlusion can be challenging, as it involves reidentifying objects with consistent identities. To address this issue, a novel multi-class object tracking methodology with occlusion handling has been proposed. This methodology employs You Only Look Once Neural Architecture Search (YOLO-NAS) and confluence-based object detection. YOLO-NAS has demonstrated superior detection with quantization-aware blocks and selective quantization, which is utilized for object tracking. Additionally, a Densely Connected Bidirectional LSTM tracker has been developed to use the feature representation and object locations from the detector. Furthermore, the methodology incorporates occlusion handling object association to re-identify objects in scenarios with occlusion or out-of-view situations. To evaluate the proposed framework, comparisons have been made with state-of-the-art models using UAV123, UAVDT, and VisDrone datasets. A detailed ablation study has been performed with UAV123 dataset. The proposed framework is observed to outperform other models with MOTA of 94.53%, Recall of 97.8%, Precision of 97.19%, F-score of 97.49% and Rel.ID of 9.26%. UAV Occlusion Object tracking Surveillance 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-4488926","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311157728,"identity":"3b1707c6-7b16-459f-a63c-d76a528d710d","order_by":0,"name":"A Ancy Micheal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYPACifp+EJVQQLwWC8aZDSAtBsRrqWDccABEE6PFnP2M8WeePxLMxudXJ354YMAgzy92AL8Wy54cM2neNgk2sxtvN0sAHWY4c3YCfi0GN3jMmHkbJHjMbpzdANKSYHCbsBawwySMZ5zd/INYLQbSPGwSBgb8vduItOVMWpnk3DaJBIkbvNssEgwkiPDL8cObP7z5U5fA3392880fFTby/NIEtDAwcEDjQgKsUoKQchBgfwCh+Q8Qo3oUjIJRMApGIgAAb3FBMJeL+GwAAAAASUVORK5CYII=","orcid":"","institution":"Vellore Institute of Technology-Chennai Campus","correspondingAuthor":true,"prefix":"","firstName":"A","middleName":"Ancy","lastName":"Micheal","suffix":""},{"id":311157729,"identity":"7e3ed44c-4ecf-4d54-87af-a698cb64ab03","order_by":1,"name":"A Annie Micheal","email":"","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"Annie","lastName":"Micheal","suffix":""},{"id":311157730,"identity":"7b5048a1-fc0e-4ce4-9fd1-f31e3b377e28","order_by":2,"name":"Anurekha Gopinathan","email":"","orcid":"","institution":"Vel Tech Rangarajan Dr. Sagunthala R\u0026D Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Anurekha","middleName":"","lastName":"Gopinathan","suffix":""},{"id":311157731,"identity":"4ed35546-8e57-4572-a9b1-900e68e0320b","order_by":3,"name":"B U Anu Barathi","email":"","orcid":"","institution":"Sathyabama Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"U Anu","lastName":"Barathi","suffix":""}],"badges":[],"createdAt":"2024-05-28 07:33:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4488926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4488926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59034588,"identity":"18f8debe-ee6c-4a65-9db6-58291b2bbd33","added_by":"auto","created_at":"2024-06-25 14:48:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":608384,"visible":true,"origin":"","legend":"","description":"","filename":"Article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4488926/v1_covered_5df021f1-d5f6-4865-a6c5-d8267cc8d277.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDeep Learning-based Multi-class Object Tracking With Occlusion Handling Mechanism in Uav Videos\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":"UAV, Occlusion, Object tracking, Surveillance","lastPublishedDoi":"10.21203/rs.3.rs-4488926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4488926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnmanned Aerial Vehicles (UAVs) play a crucial role in tracking-based applications, particularly in real-time situations such as rescue missions and surveillance. 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