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dc.contributor.authorGarcía Aguilar, Iván
dc.contributor.authorGarcía-González, Jorge
dc.contributor.authorMedina, Daniel
dc.contributor.authorLuque-Baena, Rafael Marcos 
dc.contributor.authorDomínguez-Merino, Enrique 
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2024-01-18T08:18:22Z
dc.date.available2024-01-18T08:18:22Z
dc.date.issued2023-11-17
dc.identifier.citationIván García-Aguilar, Jorge García-González, Daniel Medina, Rafael Marcos Luque-Baena, Enrique Domínguez, Ezequiel López-Rubio, Detection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent size, Neurocomputing, Volume 567, 2024, 127057, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2023.127057es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28843
dc.description.abstractAutonomous driving requires information such as the velocity of other vehicles to prevent potential hazards. This work proposes a real-time deep learning-based framework to estimate vehicle speeds from image captures through an onboard camera. Vehicles are detected and tracked by the proposed deep neural networks and a tracking algorithm, which analyzes the trajectories. Finally, a linear regression model estimates the speed of a vehicle based on its position and size in the camera frame. This proposal has been tested with two sequences of the Prevention dataset with satisfactory results. The system can estimate the speed of multiple vehicles simultaneously. It can be integrated easily with onboard computer systems, thus allowing to development of a low-cost solution for speed estimation in an everyday vehicle. The potential applications include vehicle safety systems, driver assistance, and autonomous driving technologies.es_ES
dc.description.sponsorshipFunding for open Access charge: Universidad de Málaga / CBUA. This work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA20-FEDERJA-108, project name Detection, characterization and prognosis value of the non-obstructive coronary disease with deep learning. It includes funds from the Euro- pean Regional Development Fund (ERDF). It is also partially supported by the University of Málaga (Spain) under grants B1-2019_01, B1- 2019_02 and B1-2021_20.es_ES
dc.language.isospaes_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectLingüística computacionales_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subjectInformática móviles_ES
dc.subject.otherObject detectiones_ES
dc.subject.otherSpeed estimationes_ES
dc.subject.otherDeep learninges_ES
dc.titleDetection of dangerously approaching vehicles over onboard cameras by speed estimation from apparent sizees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doi10.1016/j.neucom.2023.127057
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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