Transfer Learning Model forAnomalous Event Recognition in Big Video Data

Transfer Learning Model forAnomalous Event Recognition in Big Video Data | 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 Transfer Learning Model forAnomalous Event Recognition in Big Video Data Roqaia Adel, Aliaa Youssif, Mohamed Mostafa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4514423/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 The primary objective of this research was to enhance video surveillance through the frequent utilization of anomalous event recognition techniques by incorporating transfer learning for recognizing human activity. Every community was extremely concerned with ensuring individual security due to the growing varieties of activities that could cause injury, from malicious acts to accidents. Standard CCTV proved insufficient due to the cost of constant monitoring and the decreasing focus of human operators over time. Automated security systems with real-time anomalous event recognition were essential to solve these problems. In this paper, ResNet50, VGG19, EfficientNetB7, and ViT_b16 models were used. These models were specifically designed to recognize anomalous events in surveillance videos. To streamline video processing, a semantic key frame extraction algorithm based on action recognition was utilized to minimize the number of frames. The algorithm leveraged enhanced features to analyze real-time anomalous events such as arrests and assaults. The proposed method recognized the difficulty presented by the large volume of frames generated by surveillance videos, requiring effective processing methods. To address the challenge of processing big video data, advanced techniques for managing and analyzing extensive video datasets were incorporated. Including both abnormal and normal video during the training and testing phase, a large number of videos in the UCF-Crime dataset were utilized for model evaluation. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 outperformed with 95.87% accuracy, with the transformer model (ViT_b16) achieving the best result. These findings illustrated the effectiveness of the proposed method in addressing the complexities of anomalous event recognition in video surveillance applications, particularly in handling the large frames generated by surveillance videos. real-time anomalous event recognition transfer learning semantic key frame extraction deep learning surveillance video processing UCF-Crime dataset 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. 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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-4514423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311399573,"identity":"97e0eb34-8b75-4e85-8cd9-0d795d22d19f","order_by":0,"name":"Roqaia Adel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie3PMWrDMBiGYQmBs7i7Cs0dXARqBuMcpIuEQZm6B2qoSkDe6jU9RCFT1wgEymLqNaVLfANPJUMpVbwW28kWiN5N5nv4MQA+31kWAMAAgHKEtG6i2H2Bz/o4ggJeL+fiQOQwaWcoJCQsTfvqJXe54s0uS8YFAhRfqSp5y427ksX3XeSmtAYzm5LXBRD4Wn2l7yV3xIoH2UEwnknMAsRXBlh86wjVjkBp+shiz36f+NpAhbn6SGlVDxFh3dLwFUIo0qVO6HboSmjFhL9syNIEsJbzlNGtu8L6/mWkyOf++3Fc5EVjfqJkSqtZvWuyuJP8i7dLduz80PSUsc/n811Gf4WcZ6g5TvWkAAAAAElFTkSuQmCC","orcid":"","institution":"Arab Academy for Science, Technology and Maritime Transport","correspondingAuthor":true,"prefix":"","firstName":"Roqaia","middleName":"","lastName":"Adel","suffix":""},{"id":311399574,"identity":"98b04f00-caa2-4b08-9ed2-f470f9254d28","order_by":1,"name":"Aliaa Youssif","email":"","orcid":"","institution":"Arab Academy for Science, Technology and Maritime Transport","correspondingAuthor":false,"prefix":"","firstName":"Aliaa","middleName":"","lastName":"Youssif","suffix":""},{"id":311399575,"identity":"cbb4473b-a2e1-4766-aacf-a45ba5311cde","order_by":2,"name":"Mohamed Mostafa","email":"","orcid":"","institution":"Arab Academy for Science, Technology and Maritime Transport","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Mostafa","suffix":""}],"badges":[],"createdAt":"2024-06-01 16:23:19","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4514423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4514423/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59018570,"identity":"c78f3d65-1305-46ed-ac59-1845592285a6","added_by":"auto","created_at":"2024-06-25 11:14:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":700460,"visible":true,"origin":"","legend":"","description":"","filename":"TransferLearningModelforAnomalousEventRecognitioninBigVideoData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4514423/v1_covered_5bc4d9c7-808a-4f9a-9761-c47ce62d6ae0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTransfer Learning Model forAnomalous Event Recognition in Big Video Data\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":true,"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":"real-time anomalous event recognition, transfer learning, semantic key frame extraction, deep learning, surveillance video processing, UCF-Crime dataset","lastPublishedDoi":"10.21203/rs.3.rs-4514423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4514423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The primary objective of this research was to enhance video surveillance through the frequent utilization of anomalous event recognition techniques by incorporating transfer learning for recognizing human activity. Every community was extremely concerned with ensuring individual security due to the growing varieties of activities that could cause injury, from malicious acts to accidents. Standard CCTV proved insufficient due to the cost of constant monitoring and the decreasing focus of human operators over time. Automated security systems with real-time anomalous event recognition were essential to solve these problems. In this paper, ResNet50, VGG19, EfficientNetB7, and ViT\\_b16 models were used. These models were specifically designed to recognize anomalous events in surveillance videos. To streamline video processing, a semantic key frame extraction algorithm based on action recognition was utilized to minimize the number of frames. The algorithm leveraged enhanced features to analyze real-time anomalous events such as arrests and assaults. The proposed method recognized the difficulty presented by the large volume of frames generated by surveillance videos, requiring effective processing methods. To address the challenge of processing big video data, advanced techniques for managing and analyzing extensive video datasets were incorporated. Including both abnormal and normal video during the training and testing phase, a large number of videos in the UCF-Crime dataset were utilized for model evaluation. EfficientNetB7 achieved 86.34\\% accuracy, VGG19 reached 87.90\\%, ResNet50 attained 90.46\\%, and ViT\\_b16 outperformed with 95.87\\% accuracy, with the transformer model (ViT\\_b16) achieving the best result. 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