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Memorias de investigación
Communications at congresses:
Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space
Year:2010
Research Areas
  • Processing and signal analysis
Information
Abstract
This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects.
International
Si
Congress
IEEE Internaional Conference on Image Processing (ICIP)
960
Place
Hong Kong (China)
Reviewers
Si
ISBN/ISSN
978-1-4244-7992-4
10.1109/ICIP.2010.5653489
Start Date
26/09/2010
End Date
29/09/2010
From page
845
To page
848
Proceedings of the Internaional Conference on Image Processing
Participants
  • Autor: Carlos Cuevas Rodriguez (UPM)
  • Autor: Narciso Garcia Santos (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)