Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

Camplani, Massimo and Blanco Adán, Carlos Roberto del and Salgado Álvarez de Sotomayor, Luis and Jaureguizar Núñez, Fernando and García Santos, Narciso (2014). Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction. "Machine Vision and Applications", v. 25 (n. 5); pp. 1197-1210. ISSN 0932-8092. https://doi.org/10.1007/s00138-013-0557-2.

Description

Title: Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction
Author/s:
  • Camplani, Massimo
  • Blanco Adán, Carlos Roberto del
  • Salgado Álvarez de Sotomayor, Luis
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Item Type: Article
Título de Revista/Publicación: Machine Vision and Applications
Date: July 2014
ISSN: 0932-8092
Volume: 25
Subjects:
Freetext Keywords: Background modeling, Foreground prediction, Mixture of Gaussian, RGB-D cameras, Microsoft Kinect, Classifier combination
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (4MB) | Preview

Abstract

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37381
DC Identifier: http://oa.upm.es/37381/
OAI Identifier: oai:oa.upm.es:37381
DOI: 10.1007/s00138-013-0557-2
Official URL: http://link.springer.com/article/10.1007%2Fs00138-013-0557-2
Deposited by: Memoria Investigacion
Deposited on: 02 Sep 2015 18:30
Last Modified: 02 Sep 2015 18:30
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM