{"data":{"id":"10.4121/1f95b126-c9fa-4fc0-bd99-7f377598a037","type":"dois","attributes":{"doi":"10.4121/1f95b126-c9fa-4fc0-bd99-7f377598a037","prefix":"10.4121","suffix":"1f95b126-c9fa-4fc0-bd99-7f377598a037","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Hesam Araghi","nameType":"Personal","givenName":"Hesam","familyName":"Araghi","nameIdentifiers":[{"nameIdentifier":"0000-0002-4539-4408","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Jan van Gemert","nameType":"Personal","givenName":"Jan","familyName":"van Gemert","nameIdentifiers":[{"nameIdentifier":"0000-0002-6913-0482","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Nergis Tömen","nameType":"Personal","givenName":"Nergis","familyName":"Tömen","nameIdentifiers":[{"nameIdentifier":"0000-0003-3916-1859","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Code supporting the paper \"Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling\""}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Electrical and Electronic Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0906"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"event camera"},{"subject":"event subsampling methods"},{"subject":"Event-based video classification"},{"subject":"event camera efficiency"},{"subject":"accuracy–efficiency trade-offs"}],"contributors":[{"name":"TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Pattern Recognition \u0026 Bioinformatics, Delft, The Netherlands","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-11","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"Software","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1109/cvprw67362.2025.00499","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://github.com/hesamaraghi/event-camera-subsampling-methods","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/1f95b126-c9fa-4fc0-bd99-7f377598a037.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["script/.py / .sh / .yaml"],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Official codebase for the CVPR 2025 workshop paper:\u003cbr\u003e\u0026gt; **Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling** \u0026gt; Hesam Araghi, Jan van Gemert, Nergis Tomen \u0026gt; Delft University of Technology \u0026gt; [Paper PDF: http://arxiv.org/abs/2505.21187\u003cbr\u003e🔍 Overview\u003cbr\u003eEvent cameras offer high temporal resolution and power efficiency, but their high event rates can overload processing systems. We explore **six hardware-friendly event subsampling methods** and assess their impact on **event-based video classification**.\u003cbr\u003e🔑 Key Contributions\u003cbr\u003e📊 Systematic evaluation of 6 causal, hardware-friendly subsampling methods.🧠 Proposal of a **causal density-based subsampling** technique to test the hypothesis that dense regions contain more information.📉 Analysis of **accuracy–efficiency trade-offs** across three benchmark datasets.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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