FGEFNet: Fine-Grained Extraction and Flow Network for Crowd Counting

FGEFNet: Fine-Grained Extraction and Flow Network for Crowd Counting | 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 FGEFNet: Fine-Grained Extraction and Flow Network for Crowd Counting Jianping Yue, Jintao Cheng, Wenli Wu, Xiaoyu Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4607436/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 Crowd counting is an important application of artificial intelligence in computer graphics and one of the most challenging research areas in the field of computer vision. Most existing methods lack consideration for fine-grained information and the role of advanced features, making it challenging to simultaneously focus on fine-grained information while extracting global information In this paper, we propose a fine-grained extraction and flow network (FGEFNet) for crowd counting. Firstly, we propose a Feature Selection Fusion Pyramid structure, which fully exploits important information in high-level feature maps and ensure the flow and fusion of fine-grained features. Secondly, we propose an Adaptive Channel Focus Module (ACFM),whcih can make the model focus on global features while also paying attention to fine-grained features. We innovatively introduce ACFM at the backend of the network in a fine-grained manner, providing fine-grained channel perception capability to detect subtle features in crowd images. Finally, we conducted extensive experiments on four widely used datasets, achieving State-Of-The-Art (SOTA) results on the SHHA datasets. It is worth noting that FGEFNet achieves a remarkable improvement by reducing Mean Absolute Error (MAE) by 2.56 and Mean Squared Error (MSE) by 7.4 on the SHHA dataset compared to the existing best model. The code and models are available at https://github.com/lele-progammer/FGEFNet crowd counting Fine-grained Adaptive Channel Focus Module Feature Selection Fusion Pyramid 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-4607436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324798650,"identity":"ea225e28-2fce-44f1-a845-11e0d8534a5c","order_by":0,"name":"Jianping Yue","email":"","orcid":"","institution":"School of Data Science and Engineering, Xingzhi College, South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Yue","suffix":""},{"id":324798652,"identity":"04ef17e6-98e4-4734-8b60-7c346d25e926","order_by":1,"name":"Jintao Cheng","email":"","orcid":"","institution":"School of Physics, South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Cheng","suffix":""},{"id":324798653,"identity":"5599b510-98d6-4bfe-bbc0-291b6fc795ec","order_by":2,"name":"Wenli Wu","email":"","orcid":"","institution":"School of Data Science and Engineering, Xingzhi College, South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Wu","suffix":""},{"id":324798655,"identity":"72f41ef1-54d0-4dbe-8cf1-dfbcd57baf39","order_by":3,"name":"Xiaoyu Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYPACGyjNRoRaHgiVRrqWwyRosWc/e/g1b9t5e/7ZPQYMH8oOM/DPbiBgC09emuXMttuJM+6cMWCcce4wg8SdA4QclmNm8LHtdoKBRI4BM2/bYQYDiQQCWvjfmBkktp2zB2v5S5QWiRzjBx/bDjBuAGlhJErLjTdmQC8kJ864kVZwsOdcOo/EDQJa2PtzjD/zlNnZ889I3vjgR5m1HP8MAlqAgE2CERodBxjgEYUfMH9g+EOMulEwCkbBKBixAACxaz8dFnwZJgAAAABJRU5ErkJggg==","orcid":"","institution":"School of Physics, South China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-06-19 17:35:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4607436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4607436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63566228,"identity":"afb51b2f-7879-4d42-9f40-96bab57a45e5","added_by":"auto","created_at":"2024-08-29 16:02:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12069475,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607436/v1_covered_b94c867c-b5a6-408c-9205-a604460e1b8a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FGEFNet: Fine-Grained Extraction and Flow Network for Crowd Counting","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, Fine-grained, Adaptive Channel Focus Module, Feature Selection Fusion Pyramid","lastPublishedDoi":"10.21203/rs.3.rs-4607436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4607436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Crowd counting is an important application of artificial intelligence in computer graphics and one of the most challenging research areas in the field of computer vision. Most existing methods lack consideration for fine-grained information and the role of advanced features, making it challenging to simultaneously focus on fine-grained information while extracting global information In this paper, we propose a fine-grained extraction and flow network (FGEFNet) for crowd counting. Firstly, we propose a Feature Selection Fusion Pyramid structure, which fully exploits important information in high-level feature maps and ensure the flow and fusion of fine-grained features. Secondly, we propose an Adaptive Channel Focus Module (ACFM),whcih can make the model focus on global features while also paying attention to fine-grained features. We innovatively introduce ACFM at the backend of the network in a fine-grained manner, providing fine-grained channel perception capability to detect subtle features in crowd images. Finally, we conducted extensive experiments on four widely used datasets, achieving State-Of-The-Art (SOTA) results on the SHHA datasets. It is worth noting that FGEFNet achieves a remarkable improvement by reducing Mean Absolute Error (MAE) by 2.56 and Mean Squared Error (MSE) by 7.4 on the SHHA dataset compared to the existing best model. The code and models are available at https://github.com/lele-progammer/FGEFNet","manuscriptTitle":"FGEFNet: Fine-Grained Extraction and Flow Network for Crowd Counting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 01:21:13","doi":"10.21203/rs.3.rs-4607436/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"826bbbf1-2aa8-4a78-8168-e3e79e1ff01f","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-29T15:54:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-10 01:21:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4607436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4607436","identity":"rs-4607436","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}