ED-DKCNN: Advancing Supervised Crowd Counting in Complex Environments

ED-DKCNN: Advancing Supervised Crowd Counting in Complex Environments | 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 ED-DKCNN: Advancing Supervised Crowd Counting in Complex Environments Ankit Tomar, Santosh Kumar, Rahul Nijhawan, Kamal Kant Verma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4134356/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 increasing urban population has led to challenges in managing crowd dynamics, especially preventing tragic incidents like stampedes. Real-time, accurate crowd counting faces obstacles such as background clutter and perspective variations. The study accepts these challenges of supervised crowd counting by examining the effectiveness of convolutional arrangements in improving accuracy by using an encoder-decoder dynamic convolutional neural network * (ED-DKCNN). The combined segmented, edge-oriented data and texture-rich features are the input for the model capable of precise crowd counting, achievable even in complex scenarios with occlusions and dense crowds. It explores low and high-level crowd features, addresses occlusion and uneven crowd distribution, and utilizes deep mining and dense complementarity for optimized people counting without density map estimation. The proposed framework harnesses intra-and inter-depth information representation through a non-increasing-order kernel arrangement, achieving state-of-the-art accuracy in people counting compared to existing methods across various datasets. The extensive experiments over free and surveillance category datasets through multiple evaluation criteria firmly * https://link.springer.com/article/10 . establish the proposed ED-DKCNN model as a state-of-the-art performer in this domain. Moreover, the proposed model significantly advances crowd-counting methodologies, offering potential applications in multi-modal data integration, real-time scenarios, privacy concerns, edge computing, cross-domain situations, and human behavior. Crowd Counting ED-DKCNN Cross Evaluation Annotation Per Object Occlusion Multi-Domain 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. 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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-4134356","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282074039,"identity":"33fbea8a-8743-48d7-a43e-f556d7a9c219","order_by":0,"name":"Ankit Tomar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACfvlnMCYbwwGGCiDNzNyAV4tkQxqSlgNnQFoY8WsxOICkheFgG4hBQItkw7E0qRs19xj4Z7clHv44rzaavx2o5UfFNpxa+BnbjknnHCtmkLhz7MCBg9uO5844zNjA2HPmNm5bmtnbpHPYEhgYbqQ3ALUcy20AamFmbMOtxeAYSMu/BAZ5sJY5x3LnE9Ryhu2YdG5bAoPBjTSgwxpqcjcQ0iI5gy3ZOrcvgcfwRlrCgTPHDuRuBGo5iM8v/BJshrdzviXIyd1IM/5QUVOXO+/84YMPflTg1gIDPFD6MJg8QFA9EqgjRfEoGAWjYBSMEAAAxNlhbOv/dx4AAAAASUVORK5CYII=","orcid":"","institution":"Graphic Era University","correspondingAuthor":true,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Tomar","suffix":""},{"id":282074042,"identity":"dfe3059f-eadc-4c0e-84df-e3d757d659f9","order_by":1,"name":"Santosh Kumar","email":"","orcid":"","institution":"Graphic Era University","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"","lastName":"Kumar","suffix":""},{"id":282074046,"identity":"c2401a10-f0ae-4653-a1ac-02f846cc6121","order_by":2,"name":"Rahul Nijhawan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Nijhawan","suffix":""},{"id":282074047,"identity":"29bb9bdf-477c-4492-b3ba-1ed0e9986e2b","order_by":3,"name":"Kamal Kant Verma","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kamal","middleName":"Kant","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2024-03-20 05:44:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4134356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4134356/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54864528,"identity":"65ba8844-41dd-4a11-af35-a3585e6bff9f","added_by":"auto","created_at":"2024-04-17 20:37:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11466715,"visible":true,"origin":"","legend":"","description":"","filename":"MachineVisionandApplicationsmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4134356/v1_covered_ae012264-a7ce-4e25-8278-59645149fe24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ED-DKCNN: Advancing Supervised Crowd Counting in Complex Environments","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":"Crowd Counting, ED-DKCNN, Cross Evaluation, Annotation Per Object, Occlusion, Multi-Domain","lastPublishedDoi":"10.21203/rs.3.rs-4134356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4134356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increasing urban population has led to challenges in managing crowd dynamics, especially preventing tragic incidents like stampedes. 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