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dc.contributor.authorBenito-Picazo, Jesús
dc.contributor.authorFernández Rodríguez, José David
dc.contributor.authorDomínguez-Merino, Enrique 
dc.contributor.authorPalomo-Ferrer, Esteban José 
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2023-09-06T07:05:27Z
dc.date.available2023-09-06T07:05:27Z
dc.date.created2023
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/10630/27439
dc.description.abstractVideo streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform object detection. In this paper, we report a host of improvements to our previous state-of-the-art software system to reliably detect and track objects in video streams from panoramic cameras, resulting in an increase in the processing framerate in a Jetson TX2 board, with respect to our previous results. Depending on the number of processes and the load profile, we observe up to a five-fold increase in the framerate.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subjectTeledetecciónes_ES
dc.subjectProcesado de imágeneses_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherEmbedded systemes_ES
dc.subject.otherObject detectiones_ES
dc.subject.otherMultiprocessinges_ES
dc.titleParallel proccessing applied to object detection with a Jetson TX2 embedded system.es_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleSOCO 2023es_ES
dc.relation.eventplaceSalamancaes_ES
dc.relation.eventdateSeptiembre 2023es_ES


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