Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

Camplani, Massimo and Blanco Adán, Carlos Roberto del and Salgado Álvarez de Sotomayor, Luis and Jaureguizar Núñez, Fernando and García Santos, Narciso (2014). Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers. "Pattern Recognition Letters", v. 50 ; pp. 23-33. ISSN 0167-8655. https://doi.org/10.1016/j.patrec.2013.09.022.

Description

Title: Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers
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: Pattern Recognition Letters
Date: December 2014
ISSN: 0167-8655
Volume: 50
Subjects:
Freetext Keywords: Region-based background modeling; Foreground prediction; Mixture of Gaussians; Mixture of experts; Mean shift; RGB-D cameras
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37436
DC Identifier: http://oa.upm.es/37436/
OAI Identifier: oai:oa.upm.es:37436
DOI: 10.1016/j.patrec.2013.09.022
Official URL: http://www.sciencedirect.com/science/article/pii/S0167865513003589
Deposited by: Memoria Investigacion
Deposited on: 12 Sep 2015 07:24
Last Modified: 01 Jan 2017 23:30
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