Object detection in videos using hybrid deep gaussian mixture ensembles

Object detection in videos using hybrid deep gaussian mixture ensembles | 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 Object detection in videos using hybrid deep gaussian mixture ensembles Arindam Chaudhuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6792616/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 During past few years’ significant usage of video monitoring has been observed in several places. Video monitoring has always helped in improvement of security specific aspects. These systems provide major helping hand in monitoring through multiple object detection methods. However, there are certain challenges related to weather conditions which needs to be addressed. In view of this in this paper, we study multiple object detection in surveillance videos through hybrid deep gaussian mixture ensembles. This method integrates multiple deep gaussian mixture components to form ensembles which helps to develop multiple object detection solutions in surveillance videos. The computational system consists of ensemble of three major deep gaussian mixture components. The ensemble pipeline is formed with augmenting, bagging and stacking methods. The ensemble consists of hybrid object detection paradigm with steps background analysis, video pre-processing, data integration, object detection, foreground detection and post-processing. The computational pipeline is successfully experimented with ViSOR and CDnet 2014 benchmarked datasets. All results are validated with accuracy, precision, sensitivity or recall, specificity, F1-Score and RMSE metrics. Several comparative studies are performed with state-of-the-art methods as well as baselines. The experimental results demonstrate superiority of this method in comparison with other methods. Ablation studies have also been performed here with superior results. This system presents robustness in multiple object detection for real life challenges such as sudden illumination variation, shadow presence, long term occlusion and formation of dynamic backgrounds. It provides cost-effective, more profitable, efficient and sustainable real time multiple object detection solutions Theoretical Computer Science Multiple object detection video surveillance video monitoring deep gaussian mixture models ensemble learning Full Text Additional Declarations The authors declare no competing interests. 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-6792616","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464635787,"identity":"c659dd01-96b3-4977-ae8b-978b136535b3","order_by":0,"name":"Arindam Chaudhuri","email":"data:image/png;base64,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","orcid":"","institution":"Bayes Labs Bangalore India","correspondingAuthor":true,"prefix":"","firstName":"Arindam","middleName":"","lastName":"Chaudhuri","suffix":""}],"badges":[],"createdAt":"2025-05-31 19:02:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6792616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6792616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83800591,"identity":"cdc40641-0549-42b0-990c-c9ff8cedc3f6","added_by":"auto","created_at":"2025-06-03 02:53:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":685979,"visible":true,"origin":"","legend":"","description":"","filename":"ObjectdetectioninvideosusinghybriddeepgaussianmixtureensemblesArindamChaudhuri.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6792616/v1_covered_32e5ce2d-5c24-47e7-bd65-ab1d8eb92452.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eObject detection in videos using hybrid deep gaussian mixture ensembles\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Technical University of Berlin, Berlin Germany","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":"Multiple object detection, video surveillance, video monitoring, deep gaussian mixture models, ensemble learning ","lastPublishedDoi":"10.21203/rs.3.rs-6792616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6792616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDuring past few years’ significant usage of video monitoring has been observed in several places. 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