UREPTrack: Unified RGB-Event Visual Tracking via PoolFormer Backbone

UREPTrack: Unified RGB-Event Visual Tracking via PoolFormer Backbone | 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Research Article UREPTrack: Unified RGB-Event Visual Tracking via PoolFormer Backbone Min Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7551764/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in The Visual Computer → Version 1 posted 10 You are reading this latest preprint version Abstract Visual Object Tracking (VOT) faces significant challenges under conditions such as fast motion, motion blur, and extreme illumination. RGB-only trackers often degrade in these scenarios, while event cameras provide microsecond latency and high dynamic range but lack rich spatial semantics. We introduce UREPTrack, a unified, single-stage, attention-free RGB-event tracker built on a lightweight PoolFormer backbone. Raw event data are voxelized into compact spatiotemporal tensors and, together with RGB template and search patches, embedded and concatenated into a single token stream processed by a shared backbone. A fully con- volutional head jointly predicts classification confidence, center offsets, and box size, eliminating the need for multi-branch Siamese pipelines and costly self-attention. UREPTrack achieves state-of-the-art performance, setting new benchmarks on COESOT (S 64.4, P 77.5, NP 76.2, BOC 23.7) at 170 FPS, VisEvent (S 55.46, SR0.5 67.01, SR0.75 46.96, P 71.58, NP 75.22), and FE108 (P 94.3, S 65.9). Ablation studies confirm (i) the complementarity of RGB and event modalities, (ii) the superiority of event voxelization over image-like alternatives, and (iii) favorable accuracy and effciency scaling across PoolFormer sizes. UREPTrack provides a practical, high-speed solution for real-time, multi-modal tracking on resource-constrained hardware. Our codes will be publicly released in https://github.com/HamadYA/UREPTrack. Dynamic Vision Sensors (DVS) Event Cameras MetaFormer PoolFormer Real-Time Tracking RGB-Event Fusion Visual Object Tracking (VOT) Voxel Grid Representation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Dec, 2025 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 06 Sep, 2025 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. 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