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

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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.

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Acknowledgments

This work has been partially supported by the Ministerio de Economía y Competitividad of the Spanish Government under the project TEC2010-20412 (Enhanced 3DTV). M. Camplani would like to acknowledge the European Union and the Universidad Politécnica de Madrid (UPM) for supporting his activities through the Marie Curie-Cofund research grant.

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Authors and Affiliations

  1. E.T.S.I Telecomunicación, Universidad Politécnica de Madrid, Grupo de Tratamiento de Imágenes, Avenida Complutense, 30, 28040 , Madrid, Spain

    Massimo Camplani, Carlos Roberto del Blanco, Luis Salgado, Fernando Jaureguizar & Narciso García

  2. Video Processing and Understanding Laboratory, Universidad Autónoma de Madrid, 28049 , Madrid, Spain

    Luis Salgado

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  1. Massimo Camplani
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  2. Carlos Roberto del Blanco
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  3. Luis Salgado
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  4. Fernando Jaureguizar
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  5. Narciso García
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Correspondence to Massimo Camplani.

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Camplani, M., del Blanco, C.R., Salgado, L. et al. Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction. Machine Vision and Applications 25, 1197–1210 (2014). https://doi.org/10.1007/s00138-013-0557-2

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  • DOI: https://doi.org/10.1007/s00138-013-0557-2

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