Publication:
LSI Far Infrared Pedestrian Dataset

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorOlmeda Reino, Daniel
dc.contributor.authorPremebida, Cristiano
dc.contributor.authorNunes, Urbano
dc.contributor.authorArmingol Moreno, José María
dc.contributor.authorEscalera Hueso, Arturo de la
dc.contributor.otherUniversidad Carlos III de Madrid. Laboratorio de Sistemas Inteligentes = Carlos III University of Madrid. Intelligent System Labes
dc.date.accessioned2013-07-23T11:00:42Z
dc.date.available2013-07-23T11:00:42Z
dc.date.issued2013-07
dc.descriptionThe database consists of FIR images collected from a vehicle driven in outdoors urban scenarios. Images were acquired with an Indigo Omega imager, with a resolution of 164x129 pixels, a grey-level scale of 14 bits, and focal length of 318 pixels. The camera was mounted on the exterior of the vehicle, to avoid infrared filtering of the windshield. Recorded images were manually annotated, where each pedestrian is labelled as a bounding box. To prevent bias introduced by border artifacts their height is subsequently upscaled by 5%. The pedestrians appear in an up-right position.en
dc.descriptionThe dataset is divided in two: (i) Classification dataset: positives and randomly sampled negatives with a fixed height-width ratio of (1/2) and rescaled to 64x32 pixels, and (ii) Detection Dataset: Original positive and negative images with annotations.en
dc.description.tableofcontentsThe classification Database is divided in a Train and a Test subset. The Train set contains 10208 positives and 43390 negatives, while the Test set contains 5944 positives and 22050 negatives. The annotated bounding boxes are resized to a constant aspect ratio (w/h) = 0.5 by changing their width appropriately. Any bounding box below 10 pixels in height is ignored. The remaining bounding boxes are resized to 64x32 pixels using bilinear interpolation. The negative samples were randomly selected from images not containing pedestrians.en
dc.identifier.urihttps://hdl.handle.net/10016/17370
dc.language.isoeng
dc.relation.datasethttps://doi.org/10.21950/VBIIBU
dc.relation.publisherversionhttp://www.uc3m.es/portal/page/portal/dpto_ing_sistemas_automatica/investigacion/lab_sist_inteligentes/repository
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherPedestrian detectionen
dc.subject.otherFar infrareden
dc.subject.otherAdvanced Driver Assistance Systemsen
dc.subject.otherADASen
dc.titleLSI Far Infrared Pedestrian Dataseten
dc.typedataset*
dspace.entity.typePublication
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