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Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorEspinosa, Jorge E.
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.contributor.authorBranch, John W.
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2019-10-03T09:14:45Z
dc.date.available2019-10-03T09:14:45Z
dc.date.issued2018-05-22
dc.descriptionThis paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)en
dc.description.abstractThis paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.en
dc.description.sponsorshipS.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors.en
dc.format.extent6
dc.identifier.bibliographicCitationEspinosa, J.E., Velastin, S.A. y Branch, J. W. (2018). Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN. In 9th International Conference on Pattern Recognition Systems, pp. 91-96.en
dc.identifier.doihttps://doi.org/10.1049/cp.2018.1292
dc.identifier.isbn978-1-78561-887-1
dc.identifier.publicationfirstpage91
dc.identifier.publicationlastpage96
dc.identifier.publicationtitle9th International Conference on Pattern Recognition Systems (ICPRS-18)en
dc.identifier.urihttps://hdl.handle.net/10016/28952
dc.identifier.uxxiCC/0000029996
dc.language.isoengen
dc.publisherThe Institution of Engineering and Technologyen
dc.relation.eventdate22-24 May 2018en
dc.relation.eventplaceValparaíso, Chile.en
dc.relation.eventtitle9th International Conference on Pattern Recognition Systems (ICPRS-18)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/PCOFUND-GA-2012-600371en
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.rights© 2018 The Institution of Engineering and Technology.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherMotorcycle classificationen
dc.subject.otherConvolutional neural networken
dc.subject.otherOccluded imagesen
dc.subject.otherFaster R-CNNen
dc.subject.otherDeep learningen
dc.titleMotorcycle detection and classification in urban Scenarios using a model based on Faster R-CNNen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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