Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers
Data(s) |
01/12/2014
31/12/1969
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Idioma(s) |
eng |
Publicador |
E.T.S.I. Telecomunicación (UPM) |
Relação |
http://oa.upm.es/37436/1/INVE_MEM_2014_196738.pdf http://www.sciencedirect.com/science/article/pii/S0167865513003589 TEC2010-20412 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2013.09.022 |
Direitos |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/embargoedAccess |
Fonte |
Pattern Recognition Letters, ISSN 0167-8655, 2014-12, Vol. 50 |
Palavras-Chave | #Telecomunicaciones |
Tipo |
info:eu-repo/semantics/article Artículo PeerReviewed |