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High-quality region-based foreground segmentation using a spatial grid of SVM classifiers
Citation
Zhang, Xiaohan and Blanco Adán, Carlos Roberto del and Cuevas Rodríguez, Carlos and Jaureguizar Núñez, Fernando and García Santos, Narciso (2014). High-quality region-based foreground segmentation using a spatial grid of SVM classifiers. In: "IEEE International Conference on Consumer Electronics (ICCE 2014)", 10/01/2014 - 13/01/2014, Las Vegas, Nevada, USA. pp. 488-489.
Description
| Title: | High-quality region-based foreground segmentation using a spatial grid of SVM classifiers |
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| Author/s: |
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| Item Type: | Presentation at Congress or Conference (Article) |
| Event Title: | IEEE International Conference on Consumer Electronics (ICCE 2014) |
| Event Dates: | 10/01/2014 - 13/01/2014 |
| Event Location: | Las Vegas, Nevada, USA |
| Title of Book: | IEEE International Conference on Consumer Electronics (ICCE 2014) |
| Título de Revista/Publicación: | 2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) |
| Date: | 2014 |
| ISSN: | 2158-3994 |
| Subjects: | |
| Faculty: | E.T.S.I. Telecomunicación (UPM) |
| Department: | Señales, Sistemas y Radiocomunicaciones |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.
More information
| Item ID: | 36202 |
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| DC Identifier: | http://oa.upm.es/36202/ |
| OAI Identifier: | oai:oa.upm.es:36202 |
| Official URL: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6776098 |
| Deposited by: | Memoria Investigacion |
| Deposited on: | 04 Jul 2015 07:46 |
| Last Modified: | 04 Jul 2015 07:46 |
