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dc.contributor.authorTilgner, Martin
dc.date.accessioned2020-04-16T07:19:35Z
dc.date.available2020-04-16T07:19:35Z
dc.date.issued2019cs
dc.identifier.citationProceedings of the 25st Conference STUDENT EEICT 2019. s. 409-412. ISBN 978-80-214-5735-5cs
dc.identifier.isbn978-80-214-5735-5
dc.identifier.urihttp://hdl.handle.net/11012/186705
dc.description.abstractThis work deals with pedestrian detection via convolutional neural network which can be used in autonomous car driving systems to improve travel safety. The work focuses on the influence of the training dataset on the resulting network behavior. The Faster R-CNN with ResNet 101 as backbone network and the SSDLite with MobileNet v2 as backbone network meta-architectures were selected for parameter testing. Both networks achieved real-time detection while accuracy was 61.92 % for the Faster R-CNN and 31.72 % for the SSDLite.en
dc.formattextcs
dc.format.extent409-412cs
dc.format.mimetypeapplication/pdfen
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 25st Conference STUDENT EEICT 2019en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectObject detectionen
dc.subjectConvolutional Neural Networken
dc.subjectMachine learningen
dc.subjectFaster R-CNNen
dc.subjectSSDLiteen
dc.titlePedestrian Detection In Image By Machine Learningen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date25.04.2019cs
but.event.titleStudent EEICT 2019cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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