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Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection
Cita
Cuevas Rodríguez, Carlos 
ORCID: https://orcid.org/0000-0001-9873-8502 and García Santos, Narciso ORCID: https://orcid.org/0000-0002-0397-894X
(2011).
Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection.
En: "2011 18th IEEE International Conference on Image Processing (ICIP)", 11/09/2011 - 14/09/2011, Bruselas, Bélgica. ISBN 978-1-4577-1304-0. pp. 1757-1760.
https://doi.org/10.1109/ICIP.2011.6115712.
ORCID: https://orcid.org/0000-0001-9873-8502 and García Santos, Narciso ORCID: https://orcid.org/0000-0002-0397-894X
(2011).
Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection.
En: "2011 18th IEEE International Conference on Image Processing (ICIP)", 11/09/2011 - 14/09/2011, Bruselas, Bélgica. ISBN 978-1-4577-1304-0. pp. 1757-1760.
https://doi.org/10.1109/ICIP.2011.6115712.
Descripción
| Título: | Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 2011 18th IEEE International Conference on Image Processing (ICIP) |
| Fechas del Evento: | 11/09/2011 - 14/09/2011 |
| Lugar del Evento: | Bruselas, Bélgica |
| Título del Libro: | Proceedings of 2011 18th IEEE International Conference on Image Processing (ICIP) |
| Fecha: | 2011 |
| ISBN: | 978-1-4577-1304-0 |
| Materias: | |
| ODS: | |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Señales, Sistemas y Radiocomunicaciones |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier
Más información
| ID de Registro: | 13469 |
|---|---|
| Identificador DC: | https://oa.upm.es/13469/ |
| Identificador OAI: | oai:oa.upm.es:13469 |
| Identificador DOI: | 10.1109/ICIP.2011.6115712 |
| URL Oficial: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 22 Nov 2012 12:34 |
| Ultima Modificación: | 21 Abr 2016 12:47 |
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