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

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dc.contributor.author Espinosa, Jorge E.
dc.contributor.author Velastin Carroza, Sergio Alejandro
dc.contributor.author Branch, John W.
dc.date.accessioned 2019-10-03T09:14:45Z
dc.date.available 2019-10-03T09:14:45Z
dc.date.issued 2018-05-22
dc.identifier.bibliographicCitation Espinosa, 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.
dc.identifier.isbn 978-1-78561-887-1
dc.identifier.uri http://hdl.handle.net/10016/28952
dc.description This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)
dc.description.abstract This 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.
dc.description.sponsorship S.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.
dc.format.extent 6
dc.language.iso eng
dc.publisher The Institution of Engineering and Technology
dc.rights © 2018 The Institution of Engineering and Technology.
dc.subject.other Motorcycle classification
dc.subject.other Convolutional neural network
dc.subject.other Occluded images
dc.subject.other Faster R-CNN
dc.subject.other Deep learning
dc.title Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1049/cp.2018.1292
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/PCOFUND-GA-2012-600371
dc.relation.projectID Gobierno de España. COFUND2013-51509
dc.type.version acceptedVersion
dc.relation.eventdate 22-24 May 2018
dc.relation.eventplace Valparaíso, Chile.
dc.relation.eventtitle 9th International Conference on Pattern Recognition Systems (ICPRS-18)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 91
dc.identifier.publicationlastpage 96
dc.identifier.publicationtitle 9th International Conference on Pattern Recognition Systems (ICPRS-18)
dc.identifier.uxxi CC/0000029996
dc.contributor.funder European Commission
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