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Vision Based Traffic Surveys in Urban Environments

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)es
dc.contributor.authorChen, Zezhi
dc.contributor.authorEllis, Tim
dc.contributor.authorVelastin Carroza, Sergio Alejandro
dc.date.accessioned2018-04-04T11:46:52Z
dc.date.available2018-04-04T11:46:52Z
dc.date.issued2016-04-28
dc.description.abstractThis paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.en
dc.description.sponsorshipThe authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. 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.en
dc.format.extent22
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationChen, Z., Ellis, T., Velastin, S.A. (2016).Vision-based traffic surveys in urban environments. Journal of Electronic Imaging, 25(5), 051206en
dc.identifier.doihttps://doi.org/10.1117/1.JEI.25.5.051206
dc.identifier.issn1017-9909
dc.identifier.publicationissue5
dc.identifier.publicationtitleJournal of Electronic Imagingen
dc.identifier.publicationvolume25
dc.identifier.urihttps://hdl.handle.net/10016/26560
dc.identifier.uxxiAR/0000021045
dc.language.isoeng
dc.publisherSPIEen
dc.relation.projectIDGobierno de España. COFUND2013-51509es
dc.relation.projectIDeu-repo/grantAgreement/EC/FP7/600371
dc.rights© 2016 Society of Photo Optical Instrumentation Engineers (SPIE) and IS&T. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibiteden
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherbackground subtractionen
dc.subject.othergaussian mixture modelen
dc.subject.othervehicle detectionen
dc.subject.othervehicle classificationen
dc.subject.othervehicle trackingen
dc.titleVision Based Traffic Surveys in Urban Environmentsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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