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dc.contributor.authorRajnoha, Martin
dc.date.accessioned2019-03-04T10:05:55Z
dc.date.available2019-03-04T10:05:55Z
dc.date.issued2018cs
dc.identifier.citationProceedings of the 24th Conference STUDENT EEICT 2018. s. 441-445. ISBN 978-80-214-5614-3cs
dc.identifier.isbn978-80-214-5614-3
dc.identifier.urihttp://hdl.handle.net/11012/138273
dc.description.abstractImage similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.en
dc.formattextcs
dc.format.extent441-445cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 24th Conference STUDENT EEICT 2018en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectsurveillanceen
dc.subjectpedestrianen
dc.subjectrecognitionen
dc.subjectSiameseen
dc.subjectdeep learningen
dc.titleRealtime Pedestrian Recognition Using Siamese Networken
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date26.04.2018cs
but.event.titleStudent EEICT 2018cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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