FF3F: Feature-Fused 3 Frame Hybrid NeuralNetwork Framework for 3D Tracking ofFast-Moving Small Objects

FF3F: Feature-Fused 3 Frame Hybrid NeuralNetwork Framework for 3D Tracking ofFast-Moving Small Objects | 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 FF3F: Feature-Fused 3 Frame Hybrid NeuralNetwork Framework for 3D Tracking ofFast-Moving Small Objects Yanghe Yan, Paul I. Ro, Kiron Ang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7943189/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 Detecting and tracking fast-moving small objects with LiDAR remains a challenge. Conventional computer vision approaches rely heavily on contour segmentation, which degrades sharply when the accessible features of the targets are limited. To address this limitation, we propose the Feature-Fused ThreeFrame (FF3F) detection algorithm, which integrates lightweight low-level neural networks and conditional convolution layers to achieve a balance between accuracy and efficiency. FF3F fuses pixel drifts, centroid displacements, and velocity estimates within a confidence-aware framework, enabling robust motion estimation even under scarce signal or noisy environments. The “three-frame” design refers to integrating temporal features across three consecutive frames, thereby strengthening recognition consistency. The approach is tested and compared against an end-to-end three-frame neural network-based detection (EE3F) model and a traditional feature-based optical flow (FBOF) algorithm. This study uses solid-state LiDAR sensors. Performance was evaluated by Precision, Recall, F1(the harmonic mean of its precision and recall rate), and IoU (Intersection over Union). When target objects are moving at 9–12 m/s and covering approximately 128 pixels, the FF3F model achieved an average recall of 0.89, outperforming other frameworks (EE3F: 0.78, FBOF: 0.59). FF3F had a latency between 27.31ms and 92.35 ms. In comparison, EE3F exhibited higher latency (59.42 ms –171.64 ms), while FBOF was faster (17.13 ms – 55.43 ms) but substantially less accurate. These results confirm that FF3F effectively balances accuracy and computational efficiency under visibility constraints. LiDAR 3D object tracking feature-fused NN safety 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-7943189","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535622804,"identity":"f673287d-0328-453a-954d-909783135f0b","order_by":0,"name":"Yanghe Yan","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Yanghe","middleName":"","lastName":"Yan","suffix":""},{"id":535622805,"identity":"a3aebd80-528c-48fc-8e44-37c6146cb8fe","order_by":1,"name":"Paul I. 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Conventional computer vision approaches rely heavily on contour segmentation, which degrades sharply when the accessible features of the targets are limited. To address this limitation, we propose the Feature-Fused ThreeFrame (FF3F) detection algorithm, which integrates lightweight low-level neural networks and conditional convolution layers to achieve a balance between accuracy and efficiency. FF3F fuses pixel drifts, centroid displacements, and velocity estimates within a confidence-aware framework, enabling robust motion estimation even under scarce signal or noisy environments. The “three-frame” design refers to integrating temporal features across three consecutive frames, thereby strengthening recognition consistency. The approach is tested and compared against an end-to-end three-frame neural network-based detection (EE3F) model and a traditional feature-based optical flow (FBOF) algorithm. This study uses solid-state LiDAR sensors. Performance was evaluated by Precision, Recall, F1(the harmonic mean of its precision and recall rate), and IoU (Intersection over Union). When target objects are moving at 9–12 m/s and covering approximately 128 pixels, the FF3F model achieved an average recall of 0.89, outperforming other frameworks (EE3F: 0.78, FBOF: 0.59). FF3F had a latency between 27.31ms and 92.35 ms. In comparison, EE3F exhibited higher latency (59.42 ms –171.64 ms), while FBOF was faster (17.13 ms – 55.43 ms) but substantially less accurate. 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