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dc.contributor.authorOrtego Hernández, Diego 
dc.contributor.authorSan Miguel Avedillo, Juan Carlos 
dc.contributor.authorMartínez Sánchez, José María 
dc.contributor.otherUAM. Departamento de Tecnología Electrónica y de las Comunicacioneses_ES
dc.date.accessioned2016-11-18T17:14:50Z
dc.date.available2016-11-18T17:14:50Z
dc.date.issued2016-06
dc.identifier.citationComputer Vision and Image Understanding 147 (2016): 23 – 37en_US
dc.identifier.issn1077-3142 (print)en_US
dc.identifier.issn1090-235X (online)en_US
dc.identifier.urihttp://hdl.handle.net/10486/675251
dc.descriptionThis is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. 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 Computer Vision and Image Understanding, VOL147 (2016) DOI 10.1016/j.cviu.2016.03.012en_US
dc.description.abstractBackground estimation in video consists in extracting a foreground-free image from a set of training frames. Moving and stationary objects may affect the background visibility, thus invalidating the assumption of many related literature where background is the temporal dominant data. In this paper, we present a temporal-spatial block-level approach for background estimation in video to cope with moving and stationary objects. First, a Temporal Analysis module obtains a compact representation of the training data by motion filtering and dimensionality reduction. Then, a threshold-free hierarchical clustering determines a set of candidates to represent the background for each spatial location (block). Second, a Spatial Analysis module iteratively reconstructs the background using these candidates. For each spatial location, multiple reconstruction hypotheses (paths) are explored to obtain its neighboring locations by enforcing inter-block similarities and intra-block homogeneity constraints in terms of color discontinuity, color dissimilarity and variability. The experimental results show that the proposed approach outperforms the related state-of-the-art over challenging video sequences in presence of moving and stationary objects.en_US
dc.description.sponsorshipThis work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R) and by the TEC department (Universidad Autónoma de Madrid).en_US
dc.format.extent41 pag.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevier B.V.en_US
dc.relation.ispartofComputer Vision and Image Understandingen_US
dc.rights© 2016 Elsevier B.V. All rights reserveden_US
dc.subject.otherBackground estimationen_US
dc.subject.otherBackground visibilityen_US
dc.subject.otherClusteringen_US
dc.subject.otherMultipathen_US
dc.subject.otherSmoothnessen_US
dc.subject.otherStationary foregrounden_US
dc.titleRejection based multipath reconstruction for background estimation in video sequences with stationary objectsen_US
dc.typearticleen_US
dc.subject.ecienciaTelecomunicacioneses_ES
dc.date.embargoend2018-06-01
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.cviu.2016.03.012
dc.identifier.doi10.1016/j.cviu.2016.03.012
dc.identifier.publicationfirstpage23
dc.identifier.publicationlastpage37
dc.identifier.publicationvolume147
dc.relation.projectIDGobierno de España. TEC2014-53176-Res_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.contributor.groupTratamiento e Interpretación de Vídeo (ING EPS-006)es_ES
dc.rights.ccReconocimiento – NoComercial – SinObraDerivadaes_ES
dc.rights.accessRightsopenAccessen
dc.authorUAMOrtego Hernández, Diego (278792)
dc.facultadUAMEscuela Politécnica Superior


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