STELLA: A modular framework for SpatioTemporal Event-based Lagrangian particLe trAcking

STELLA: A modular framework for SpatioTemporal Event-based Lagrangian particLe 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 STELLA: A modular framework for SpatioTemporal Event-based Lagrangian particLe trAcking Sebastian Sachs, Steffen Jung, Max Kahl, Margret Keuper, Christian Willert, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8886185/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Event-based cameras have emerged as a powerful tool for object detection and tracking in autonomous driving, robotics, and experimental physics. In particular, they facilitate the study of complex turbulent fluid flows by enabling the tracking of numerous tiny tracer particles, while benefiting from the superior temporal resolution, high dynamic range, and low data rate of the asynchronous event stream. However, exploiting the sparse event stream requires precise and efficient data processing pipelines that either accumulate events into a dense representation or process them directly by clustering algorithms. In this study, we present a modular framework for SpatioTemporal Event-based Lagrangian particLe trAcking (STELLA), which integrates detection and tracking strategies from both pipelines into a unified tracking system. To benchmark the proposed framework, we introduce demanding synthetic and experimental datasets covering the motion of numerous particles, which are made publicly available. Leveraging the rich ground truth of these datasets, established recurrent vision transformer and heat conduction-based detection architectures are trained and applied to particle tracking in fluid flows for the first time. Using STELLA, robust and reliable particle tracking is demonstrated, achieving subpixel-accurate tracks and a mean absolute error in the predicted velocity down to \SI{1.9}{\percent} of the peak velocity. Which is the best-performing processing pipeline strongly depends on the dynamics and composition of the considered dataset. In particular, approaches based on dense representations yield accurate tracks for high-frequency periodic particle motions. Conversely, direct processing of the event stream enables simultaneous tracking of more than 900 particles in the wake of a cylinder, with uncertainties comparable to state-of-the-art particle tracking velocimetry (PTV) using a high-speed camera. Despite significant spatial and temporal velocity gradients, slow- and fast-moving particles are precisely tracked in the event stream, challenging conventional approaches using frame-based cameras. Hence, the openly available framework STELLA paves the way for a versatile and easily accessible application of event-based cameras for flow diagnostics. event-based vision neuromorphic vision dynamic vision sensor particle tracking velocimetry Full Text Additional Declarations No competing interests reported. Supplementary Files S2chopperwheel.mp4 S1synthdata.mp4 S4wakeflowRe20600.mp4 S3wakeflowRe3100.mp4 ExFEventBasedSI.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers invited by journal 01 Mar, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 15 Feb, 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. 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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-8886185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595772622,"identity":"4762fe31-2bd2-4cd6-bda5-802a987ed99d","order_by":0,"name":"Sebastian 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