Mostrar el registro sencillo del ítem
Gait recognition from corrupted silhouettes: a robust statistical approach
dc.contributor.author | Ortells Lorenzo, Javier | |
dc.contributor.author | Mollineda, Ramón A. | |
dc.contributor.author | Mederos, Boris | |
dc.contributor.author | Martín Félez, Raúl | |
dc.date.accessioned | 2017-01-09T10:55:52Z | |
dc.date.available | 2017-01-09T10:55:52Z | |
dc.date.issued | 2016-07 | |
dc.identifier.citation | ORTELLS, Javier, et al. Gait recognition from corrupted silhouettes: a robust statistical approach. Machine Vision and Applications, 2016, p. 1-19. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/165264 | |
dc.description.abstract | This 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.sponsorShip | This 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.extent | 20 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Verlag | ca_CA |
dc.relation.isPartOf | Machine Vision and Applications, 2016 | ca_CA |
dc.rights | © 2017 Springer International Publishing AG. Part of Springer Nature. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Gait recognition | ca_CA |
dc.subject | Model-free | ca_CA |
dc.subject | Noisy silhouettes | ca_CA |
dc.subject | Occluded silhouettes | ca_CA |
dc.subject | Robust statistics | ca_CA |
dc.title | Gait recognition from corrupted silhouettes: a robust statistical approach | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.1007/s00138-016-0798-y | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | http://link.springer.com/article/10.1007/s00138-016-0798-y | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
INIT_Articles [754]
-
LSI_Articles [366]
Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics