High-Altitude Parabolic Detection: An Innovative Algorithm That Combines MOG-CNN and Enhanced SORT

High-Altitude Parabolic Detection: An Innovative Algorithm That Combines MOG-CNN and Enhanced SORT | 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 High-Altitude Parabolic Detection: An Innovative Algorithm That Combines MOG-CNN and Enhanced SORT QiGuang Zhu, Meng Liu, WenLong Wu, WeiDong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4470657/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 Addressing the challenges of complex backgrounds, small parabolic targets, indistinct appearance features of parabolas, and easy loss of parabolic tracking in high-altitude parabolic object detection, this paper proposes a hybrid approach integrating the Mixture of Gaussians Background Modeling (MOG) algorithm with neural networks and an improved Simple Online and Realtime Tracking (SORT) algorithm for parabolic tracking. Firstly, to mitigate the issues of small target parabolas and complex backgrounds, a region-specific conditional filtering is introduced to reduce non-parabolic foreground in foreground detection while preserving parabolic foreground. Secondly, to tackle the problem of indistinct appearance features of parabolas, a multi-frame channel fusion technique is employed to enhance motion features, and a lightweight classification network is designed to differentiate parabolic objects. Finally, to address the challenge of easy loss of parabolic tracking, the state space and matching metrics of SORT are improved to better match parabolic trajectories. Experimental results demonstrate that the improved parabolic detection method reduces detection quantity by 97\% compared to the original MOG algorithm while exhibiting a 7\% decrease in recall rate. Additionally, compared to the original SORT algorithm, the improved parabolic tracking method reduces the number of ID switches by 50%, increases the MOTA metric by 8%, and increases the TIOU metric by 7%. Code: https://figshare.com/articles/dataset/High-Altitude Object Detection algorithm/25778430. Dataset:https://figshare.com/articles/dataset/High-Altitude Object Detection Dataset/25778289. high-altitude parabolic object detection small object detection MOG SORT 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. 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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-4470657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309573723,"identity":"98f9978f-eb85-4c7e-8f61-4d97eeee0368","order_by":0,"name":"QiGuang Zhu","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"QiGuang","middleName":"","lastName":"Zhu","suffix":""},{"id":309573724,"identity":"cdfa7ab1-565d-458d-b045-cf9aaee29e95","order_by":1,"name":"Meng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACPmYog429+eCDD8RoYYNp4eM5lmw4gygtMIacRI6ZNA9RWth5TDd83FHLwAbSYlNmzcDf3p1AwGE8ZjdnnjnOwMbzrNg651w6g8SZsxsIarnN23YMaF3yxtu5bYcZDCRyidDyF6SFIcFA2pJoLYxtNQxsHClG0ozEaWEru9nbdoCHDRTIPefSeQj6hZ//8LYbP9vq5OTbgVH5o8xajr+9F78WKDjMA3cnMcpBoA7uTmJ1jIJRMApGwQgCAKB8PxbCXb/JAAAAAElFTkSuQmCC","orcid":"","institution":"Yanshan University","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Liu","suffix":""},{"id":309573727,"identity":"35c80607-aac8-409b-ae62-9b49b118854c","order_by":2,"name":"WenLong Wu","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"WenLong","middleName":"","lastName":"Wu","suffix":""},{"id":309573728,"identity":"24c4b81b-143e-4eb1-8147-e4377f6da1e2","order_by":3,"name":"WeiDong Chen","email":"","orcid":"","institution":"Yanshan University","correspondingAuthor":false,"prefix":"","firstName":"WeiDong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-24 07:18:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4470657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4470657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70999393,"identity":"c0b1febf-63b3-40ea-96da-32a74d0c8563","added_by":"auto","created_at":"2024-12-10 05:25:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5982464,"visible":true,"origin":"","legend":"","description":"","filename":"article2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4470657/v1_covered_35a5f404-1fc1-45ab-833b-5b7805270e52.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-Altitude Parabolic Detection: An Innovative Algorithm That Combines MOG-CNN and Enhanced SORT","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":" high-altitude parabolic object detection, small object detection, MOG, SORT","lastPublishedDoi":"10.21203/rs.3.rs-4470657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4470657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing the challenges of complex backgrounds, small parabolic targets, indistinct appearance features of parabolas, and easy loss of parabolic tracking in high-altitude parabolic object detection, this paper proposes a hybrid approach integrating the Mixture of Gaussians Background Modeling (MOG) algorithm with neural networks and an improved Simple Online and Realtime Tracking (SORT) algorithm for parabolic tracking. Firstly, to mitigate the issues of small target parabolas and complex backgrounds, a region-specific conditional filtering is introduced to reduce non-parabolic foreground in foreground detection while preserving parabolic foreground. Secondly, to tackle the problem of indistinct appearance features of parabolas, a multi-frame channel fusion technique is employed to enhance motion features, and a lightweight classification network is designed to differentiate parabolic objects. Finally, to address the challenge of easy loss of parabolic tracking, the state space and matching metrics of SORT are improved to better match parabolic trajectories. Experimental results demonstrate that the improved parabolic detection method reduces detection quantity by 97\\% compared to the original MOG algorithm while exhibiting a 7\\% decrease in recall rate. Additionally, compared to the original SORT algorithm, the improved parabolic tracking method reduces the number of ID switches by 50%, increases the MOTA metric by 8%, and increases the TIOU metric by 7%.\u003c/p\u003e\n\u003cp\u003eCode: https://figshare.com/articles/dataset/High-Altitude Object Detection algorithm/25778430. 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