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Memorias de investigación
Communications at congresses:
Versatile Bayesian classifier for moving object detection by non-parametric background-foreground modeling
Year:2012
Research Areas
  • Engineering,
  • Processing and signal analysis
Information
Abstract
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.
International
Si
Congress
IEEE International Conference on Image Processing
960
Place
Orlando (FL), USA
Reviewers
Si
ISBN/ISSN
978-1-4673-2533-2
10.1109/ICIP.2012.6466858
Start Date
30/09/2012
End Date
03/10/2012
From page
313
To page
316
Proceedings of the 2010 IEEE International Conference on Image Processing, ICIP 2012
Participants
  • Autor: Carlos Cuevas Rodriguez (UPM)
  • Autor: Raúl Mohedano Del Pozo (UPM)
  • Autor: Narciso Garcia Santos (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)
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