Precise 2D Mouse Pose Estimation via Multi-Scale Context and Sensitive-Aware Loss from Low Illumination Environment

Precise 2D Mouse Pose Estimation via Multi-Scale Context and Sensitive-Aware Loss from Low Illumination Environment | 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 Precise 2D Mouse Pose Estimation via Multi-Scale Context and Sensitive-Aware Loss from Low Illumination Environment Yubin Geng, Jiaxin Deng, Zhicheng Wang, Junbiao Pang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9078830/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Markerless pose estimation has emerged as a promising methodology for quantifying the behavior of freely moving mice. However, achieving scientifically precise 2D Mouse Pose Estimation (MPE) remains challenging, primarily due to the scarcity of large scale benchmark datasets and the underdevelopment of techniques tailored to animal behavior experiments. Existing pose estimation techniques developed for Human Pose Estimation (HPE) are rarely directly transferable to mice. One key distinction between HPE and MPE stems from the stricter precision requirements in animal behavior studies relative to human centric scenarios. In this work, we propose a novel framework that integrates Multi-Scale Context (MSC) with Sensitive-Aware Loss (SAL) to tackle this challenge. Specifically, the MSC exploits multi-scale contextual information to capture discriminative keypoint representations, while the SAL alleviates the extreme class imbalance inherently encountered in precise keypoint localization, thereby facilitating accurate localization. Experiments conducted on two public real world datasets demonstrate that our approach achieves accurate mouse keypoint localization under extremely low illumination conditions (0–15 lux). Animal Behavior Analysis Mouse Pose Estimation Gaussian Heatmap Context Information Class Imbalance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 09 Mar, 2026 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-9078830","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619981239,"identity":"67f22d1d-56ef-4775-a0f6-07e9a4cd1f23","order_by":0,"name":"Yubin Geng","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yubin","middleName":"","lastName":"Geng","suffix":""},{"id":619981241,"identity":"91ce5118-8b4a-44e5-9ed0-80d700e250f4","order_by":1,"name":"Jiaxin Deng","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Deng","suffix":""},{"id":619981243,"identity":"96abaad6-0ec7-4515-aff6-7ff7cca4658b","order_by":2,"name":"Zhicheng Wang","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhicheng","middleName":"","lastName":"Wang","suffix":""},{"id":619981248,"identity":"48469973-6cb4-4cc8-84d9-458af7f15b6d","order_by":3,"name":"Junbiao Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACxgYGNoYEGO9jAxuYliBaC+NMoBYeQlqAgA3OYuZtYCCshXlG7rEHD2psGMzZzx5+bbuDL3E/A/PB2zwMdnk4HTYjL90g4Vgag2VPXpp17hm2xB4GtmRrHobkYtxacswkEtgOMxgcyDEzzm0DaeExk+ZhOJDYgFfLP6CW82/MjC3BWvi/EdaS2AbUciPH+DEjxBY2/Fp63pgbJPYB/TLjjRlj7xk2457DbMaWcwyScWoxbM8xe/jjGzDE+HOMP/zccUy2vb354Y03FXa4tUAl6jcAIwgYHceA4Q7iG+BQDwTyMAZQDfMHBoYa3EpHwSgYBaNgxAIA9ClStEgFhTcAAAAASUVORK5CYII=","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junbiao","middleName":"","lastName":"Pang","suffix":""}],"badges":[],"createdAt":"2026-03-10 04:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9078830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9078830/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961554,"identity":"ea46c139-a775-40d0-95cb-32e6a015bd06","added_by":"auto","created_at":"2026-04-15 09:26:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":670873,"visible":true,"origin":"","legend":"","description":"","filename":"MultimediaSystemsMousePose.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9078830/v1_covered_7bd914eb-98a1-4521-9913-6abf7531b778.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Precise 2D Mouse Pose Estimation via Multi-Scale Context and Sensitive-Aware Loss from Low Illumination Environment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Animal Behavior Analysis, Mouse Pose Estimation, Gaussian Heatmap, Context Information, Class Imbalance","lastPublishedDoi":"10.21203/rs.3.rs-9078830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9078830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMarkerless pose estimation has emerged as a promising methodology for quantifying the behavior of freely moving mice. 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