Stationary foreground detection using background subtraction and temporal difference in video surveillance
Metadatos
Title:
Stationary foreground detection using background subtraction and temporal difference in video surveillance
Author:
Bayona Gómez, Álvaro; Sanmiguel, Juan Carlos; Martínez, José M.
Entity:
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher:
Institute of Electrical and Electronics Engineers
Date:
2010
Citation:
10.1109/ICIP.2010.5650699
17th IEEE International Conference on Image Processing, ICIP 2010. IEEE, 2010. 4657-4660
ISSN:
1522-4880
ISBN:
978-1-4244-7993-1 (online); 978-1-4244-7992-4 (print)
DOI:
10.1109/ICIP.2010.5650699
Funded by:
Work supported by the Spanish Government (TEC2007- 65400 SemanticVideo), by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de
video aplicadas a la seguridad”, by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund.
Editor's Version:
http://dx.doi.org/10.1109/ICIP.2010.5650699
Subjects:
Background subtraction; Frame difference; Stationary foreground detection; Video surveillance; Telecomunicaciones
Note:
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and Martínez, "Stationary foreground detection using background subtraction and temporal difference in video surveillance", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 4657 - 4660
Rights:
© 2010 IEEE
Abstract:
In this paper we describe a new algorithm focused on obtaining stationary foreground regions, which is useful for applications like the detection of abandoned/stolen objects and parked vehicles. Firstly, a sub-sampling scheme based on background subtraction techniques is implemented to obtain stationary foreground regions. Secondly, some modifications are introduced on this base algorithm with the purpose of reducing the amount of stationary foreground detected. Finally, we evaluate the proposed algorithm and compare results with the base algorithm using video surveillance sequences from PETS 2006, PETS 2007 and I-LIDS for AVSS 2007 datasets. Experimental results show that the proposed algorithm increases the detection of stationary foreground regions as compared to the base algorithm.
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