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Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

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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.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-09-27T10:06:13Z
dc.date.available2019-09-27T10:06:13Z
dc.date.issued2017-11-29
dc.descriptionThis paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017)en
dc.description.abstractThis paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic.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 wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we 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.extent13
dc.identifier.bibliographicCitationEspinosa, J.E., Velastin, S.A. y Branch, J.W. (2017). Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study. In Advances in Visual Informatics. Lecture Notes in Computer Science,10645, pp. 3-15.en
dc.identifier.doihttps://doi.org/10.1007/978-3-319-70010-6_1
dc.identifier.isbn978-3-319-70009-0
dc.identifier.publicationfirstpage3
dc.identifier.publicationlastpage15
dc.identifier.publicationtitleAdvances in Visual Informaticsen
dc.identifier.publicationvolume10645
dc.identifier.urihttps://hdl.handle.net/10016/28912
dc.identifier.uxxiCC/0000029979
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate28–30 November 2017en
dc.relation.eventplaceMalaysia, Bangien
dc.relation.eventtitle5th International Visual Informatics Conference (IVIC 2017)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/600371es
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.rights© Springer International Publishing AG 2017en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherConvolutional neural networken
dc.subject.otherFeature extractionen
dc.subject.otherVehicle classificationen
dc.titleVehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Studyen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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