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dc.contributor.authorŠkrabánek, Pavelcs
dc.contributor.authorDoležel, Petrcs
dc.contributor.authorNěmec, Zdeněkcs
dc.contributor.authorŠtursa, Dominikcs
dc.date.accessioned2020-12-02T11:55:31Z
dc.date.available2020-12-02T11:55:31Z
dc.date.issued2020-10-14cs
dc.identifier.citationJOURNAL OF ADVANCED TRANSPORTATION. 2020, vol. 2020, issue 1, p. 1-13.en
dc.identifier.issn0197-6729cs
dc.identifier.other166258cs
dc.identifier.urihttp://hdl.handle.net/11012/195743
dc.description.abstractCounting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. To allow mass utilization of passenger flow monitoring systems, their cost must be low. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. This approach is a representative of classical methods. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. To allow verification of theoretical findings, we construct an engineering prototype of the system.en
dc.formattextcs
dc.format.extent1-13cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherWiley-Hindawics
dc.relation.ispartofJOURNAL OF ADVANCED TRANSPORTATIONcs
dc.relation.urihttps://www.hindawi.com/journals/jat/2020/8843113/cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectsupport vector machinesen
dc.subjectconvolutional neural networken
dc.subjecthistograms of oriented gradientsen
dc.subjectperson flow monitoring systemen
dc.subjectobject recognitionen
dc.titlePerson Detection for an Orthogonally Placed Monocular Cameraen
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
sync.item.dbidVAV-166258en
sync.item.dbtypeVAVen
sync.item.insts2020.12.02 12:55:31en
sync.item.modts2020.12.02 12:14:15en
dc.coverage.issue1cs
dc.coverage.volume2020cs
dc.identifier.doi10.1155/2020/8843113cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0197-6729/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International