DocumentCode :
2477031
Title :
Spatio-temporal patches for night background modeling by subspace learning
Author :
Zhao, Youdong ; Gong, Haifeng ; Lin, Liang ; Jia, Yunde
Author_Institution :
Sch. of Comput. Sci., Beijing Inst. of Technol., Beijing
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, a novel background model on spatio-temporal patches is introduced for video surveillance, especially for night outdoor scene, where extreme lighting conditions often cause troubles. The spatio-temporal patch, called brick, is presented to simultaneously capture spatio-temporal information in surveillance video. The set of bricks of a given background patch, under all possible lighting conditions, lies in a low-dimensional subspace, which can be learned by online subspace learning. The proposed method can efficiently model the background and detect the appearance and motion variance caused by foreground. Experimental results on real data show that the proposed method is insensitive to dramatic lighting changes and achieves superior performance to two classical methods.
Keywords :
learning (artificial intelligence); video surveillance; lighting conditions; motion variance; night background modeling; online subspace learning; spatio-temporal information; spatio-temporal patches; video surveillance; Computer science; Eigenvalues and eigenfunctions; Image motion analysis; Information technology; Laboratories; Layout; Lighting; Motion detection; Partial response channels; Video surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761197
Filename :
4761197
Link To Document :
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