Gait recognition from corrupted silhouettes: a robust statistical approach
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Ortells Lorenzo, Javier; Mollineda, Ramón A.; Mederos, Boris; Martín Félez, Raúl
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Gait recognition from corrupted silhouettes: a robust statistical approachFecha de publicación
2016-07Editor
Springer VerlagCita bibliográfica
ORTELLS, Javier, et al. Gait recognition from corrupted silhouettes: a robust statistical approach. Machine Vision and Applications, 2016, p. 1-19.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://link.springer.com/article/10.1007/s00138-016-0798-yVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Machine Vision and Applications, 2016Derechos de acceso
© 2017 Springer International Publishing AG. Part of Springer Nature.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- INIT_Articles [754]
- LSI_Articles [366]