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Descripción
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| Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Conference on Image Processing |
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Tipo de participación
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960 |
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Lugar del congreso
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Orlando (FL), USA |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4673-2533-2 |
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DOI
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10.1109/ICIP.2012.6466858 |
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Fecha inicio congreso
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30/09/2012 |
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Fecha fin congreso
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03/10/2012 |
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Desde la página
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313 |
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Hasta la página
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316 |
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Título de las actas
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Proceedings of the 2010 IEEE International Conference on Image Processing, ICIP 2012 |