Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani, Massimo y Salgado Álvarez de Sotomayor, Luis (2014). Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers. "Journal of Visual Communication and Image Representation", v. 25 (n. 1); pp. 122-136. ISSN 1047-3203. https://doi.org/10.1016/j.jvcir.2013.03.009.

Descripción

Título: Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers
Autor/es:
  • Camplani, Massimo
  • Salgado Álvarez de Sotomayor, Luis
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Visual Communication and Image Representation
Fecha: Enero 2014
Volumen: 25
Materias:
Palabras Clave Informales: RGB-D cameras; Microsoft Kinect; Background/foreground segmentation; Combination of classifiers; RGB-D dataset; Mixture of Gaussian; Color and depth data combination; Object detection
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaTEC2010-20412Sin especificarSin especificarSin especificar

Más información

ID de Registro: 37450
Identificador DC: http://oa.upm.es/37450/
Identificador OAI: oai:oa.upm.es:37450
Identificador DOI: 10.1016/j.jvcir.2013.03.009
URL Oficial: http://www.sciencedirect.com/science/article/pii/S1047320313000497
Depositado por: Memoria Investigacion
Depositado el: 12 Sep 2015 07:56
Ultima Modificación: 01 Feb 2016 23:56
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