Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking

Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking | 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 Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking Ahmed O. Elsaid, Mohamed M. Fouad, Tarek S. Ghoniemy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3836623/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 Correlation Filter-based Trackers have shown impressive results in the object tracking research, outperforming classical trackers in several benchmarks. However, accurately tracking objects with deformation, fast motion, or occlusion remains a main challenge in the process of tracking. The cyclic suggestion of training samples used in correlation filter tracking usually lead to undesirable boundary effects, that significantly reduce the tracking efficiency. To address this issues, a hybrid attention-based correlation filter method (Att-DCF) is proposed for robust object tracking. By developing a discrimination filter and reducing the limitations of irrelevant features, the proposed approach provides more accurate and consistent estimated target locations. The proposed Att-DCF tracker is evaluated using the recent and standard datasets, OTB-100, Temple-Colour128, and UAV123. Compared to the baseline DCF tracker, the proposed tracker achieves an improvement of 2.71$%$ and 1.38$%$ in terms of area under the curve and precision measures using the OTB-100 dataset, 3.99$%$ and 3.43$%$ using the Temple-Colour128, and 4.72$%$ and 1.76$%$ using the UAV123, respectively. Full Text Additional Declarations No competing interests reported. Supplementary Files CoverLetter.pdf SpringerWCPAttDCFpaper.pdf 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-3836623","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265896961,"identity":"81416f39-1377-439c-9bb9-64f572d82bab","order_by":0,"name":"Ahmed O. 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