A corpus for benchmarking of people detection algorithms

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dc.contributor.author García-Martín, Álvaro
dc.contributor.author Martínez, José M.
dc.contributor.author Bescós, Jesús
dc.contributor.other UAM. Departamento de Tecnología Electrónica y de las Comunicaciones es_ES
dc.date.accessioned 2015-06-11T15:44:44Z
dc.date.available 2015-06-11T15:44:44Z
dc.date.issued 2012-01-15
dc.identifier.citation Pattern Recognition Letters 33.2 (2012): 152–156 en_US
dc.identifier.issn 0167-8655 (print) en_US
dc.identifier.issn 1872-7344 (online) en_US
dc.identifier.uri http://hdl.handle.net/10486/666741
dc.description This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, 33, 2 (2012) DOI: 10.1016/j.patrec.2011.09.038 en_US
dc.description.abstract This paper describes a corpus, dataset and associated ground-truth, for the evaluation of people detection algorithms in surveillance video scenarios, along with the design procedure followed to generate it. Sequences from scenes with different levels of complexity have been manually annotated. Each person present at a scene has been labeled frame by frame, in order to automatically obtain a people detection ground-truth for each sequence. Sequences have been classified into different complexity categories depending on critical factors that typically affect the behavior of detection algorithms. The resulting corpus, which exceeds other public pedestrian datasets in the amount of video sequences and its complexity variability, is freely available for benchmarking and research purposes under a license agreement. en_US
dc.description.sponsorship This work has been partially supported by the Cátedra UAM-Infoglobal (“Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad”), by the Ministerio de Ciencia e Innovación of the Spanish Goverment (TEC2011-25995 EventVideo: “Estrategias de segmentación, detección y seguimientos de objetos en entornos complejos para la detección de eventos en videovigilancia y monitorización”) and by the Universidad Autónoma de Madrid (“FPI-UAM: Programa propio de ayudas para la Formación de Personal Investigador”). en_US
dc.format.extent 8 pág. es_ES
dc.format.mimetype application/pdf en
dc.language.iso eng en
dc.publisher Elsevier BV en_US
dc.relation.ispartof Pattern Recognition Letters en_US
dc.rights © 2012 Elsevier B.V. All rights reserved en_US
dc.subject.other People detection en_US
dc.subject.other Ground-truth en_US
dc.subject.other Corpus en_US
dc.subject.other Dataset en_US
dc.subject.other Surveillance video en_US
dc.title A corpus for benchmarking of people detection algorithms en_US
dc.type article en_US
dc.subject.eciencia Telecomunicaciones es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.patrec.2011.09.038
dc.identifier.doi 10.1016/j.patrec.2011.09.038
dc.identifier.publicationfirstpage 152
dc.identifier.publicationissue 2
dc.identifier.publicationlastpage 156
dc.identifier.publicationvolume 33
dc.type.version info:eu-repo/semantics/acceptedVersion en
dc.contributor.group Tratamiento e Interpretación de Vídeo (ING EPS-006) es_ES
dc.rights.cc Reconocimiento – NoComercial – SinObraDerivada es_ES
dc.rights.accessRights openAccess en
dc.authorUAM Bescós Cano, Jesús (260863)


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