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dc.contributor.authorOrtells Lorenzo, Javier
dc.contributor.authorMollineda Cárdenas, Ramón A.
dc.contributor.authorMederos, Boris
dc.contributor.authorMartín Félez, Raúl
dc.date.accessioned2017-01-09T10:55:52Z
dc.date.available2017-01-09T10:55:52Z
dc.date.issued2016-07
dc.identifier.citationORTELLS, Javier, et al. Gait recognition from corrupted silhouettes: a robust statistical approach. Machine Vision and Applications, 2016, p. 1-19.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/165264
dc.description.abstractThis paper introduces a method based on robust statistics to build reliable gait signatures from averaging silhouette descriptions, mainly when gait sequences are affected by severe and persistent defects. The term robust refers to the ability of reducing the impact of silhouette defects (outliers) on the average gait pattern, while taking advantage of clean silhouette regions. An extensive experimental framework was defined based on injecting three types of realistic defects (salt and pepper noise, static occlusion, and dynamic occlusion) to clean gait sequences, both separately in an easy setting and jointly in a hard setting. The robust approach was compared against two other operation modes: (1) simple mean (weak baseline) and (2) defect exclusion (strong benchmark). Three gait representation methods based on silhouette averaging were used: Gait Energy Image (GEI), Gradient Histogram Energy Image (GHEI), and the joint use of GEI and HOG descriptors. Quality of gait signatures was assessed by their discriminant power in a large number of gait recognition tasks. Nonparametric statistical tests were applied on recognition results, searching for significant differences between operation modes.ca_CA
dc.description.sponsorShipThis work has been supported by the grants P1-1B2012-22 and PREDOC/2012/05 from Universitat Jaume I, PROMETEOII/2014/062 from Generalitat Valenciana, and TIN2013-46522-P from Spanish Ministry of Economy and Competitiveness.ca_CA
dc.format.extent20 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Verlagca_CA
dc.relation.isPartOfMachine Vision and Applications, 2016ca_CA
dc.rights© 2017 Springer International Publishing AG. Part of Springer Nature.ca_CA
dc.subjectGait recognitionca_CA
dc.subjectModel-freeca_CA
dc.subjectNoisy silhouettesca_CA
dc.subjectOccluded silhouettesca_CA
dc.subjectRobust statisticsca_CA
dc.titleGait recognition from corrupted silhouettes: a robust statistical approachca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1007/s00138-016-0798-y
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/article/10.1007/s00138-016-0798-yca_CA


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  • LSI_Articles [265]
    Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics

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