A method for measuring fish motion parameters based on binocular stereo vision

A method for measuring fish motion parameters based on binocular stereo vision | 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 A method for measuring fish motion parameters based on binocular stereo vision xin wu, yufeng xie, Shenli Fan, Qiu Hu, Weiming Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7823189/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 To address the issues of high hardware requirements and low measurement efficiency in the process of measuring fish movement parameters, a method based on binocular vision for measuring fish movement parameters is proposed in this study. First, fish keypoints data in videos are identified and tracked based on the open-source machine learning tool DeepLabCut by collecting, annotating, training images and video data. Linear interpolation and feedforward neural networks are introduced to the keypoints data processing to fill the missing keypoints from the video. The three-dimensional coordinates of the fish keypoints were obtained via keypoints of left and right images based on binocular stereo vision principle. Finally, movement parameters, such as the speed, acceleration, and rotational speed of the fish, were calculated using the three-dimensional coordinates of each frame. The effectiveness of this method has been verified through fish data in a laboratory environment, and the results show that it is more accurate than traditional manual measurements. This method provides a new technique for research in the fields of fish behavioral ecology ,aquaculture science and technology. It is also helpful for real-time monitoring and assessment of the health status of aquatic ecosystems. DeepLabCut Deep learning Motion parameters Binocular stereo vision 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-7823189","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533696241,"identity":"92bac20a-7758-464b-ad86-8ae12c434401","order_by":0,"name":"xin wu","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"wu","suffix":""},{"id":533696242,"identity":"3fac6dd4-1415-4d8c-9be4-98c752ccfd80","order_by":1,"name":"yufeng xie","email":"","orcid":"","institution":"Zhejiang Sci-Tech 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