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Proceedings Paper

A new incremental principal component analysis with a forgetting factor for background estimation
Author(s): Takashi Toriu; Thi Thi Zin; Hiromitsu Hama
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Paper Abstract

Background subtraction is one of commonly used techniques for many applications such as human detection in images. For background estimation, principal component analysis (PCA) is an available method. Since the background sometimes changes according to illumination change or due to a newly appeared stationary article, the eigenspace should be updated momentarily. A naïve algorithm for eigenspace updating is to update the covariance matrix. Then, the eigenspace is updated by solving the eigenvalue problem for the covariance matrix. But this procedure is very time consuming because covariance matrix is a very large size matrix. In this paper we propose a novel method to update the eigenspace approximately with exceedingly low computational cost. Main idea to decrees computational cost is to approximate the covariance matrix by low dimensional matrix. Thus, computational cost to solve eigenvalue problem becomes exceedingly decrease. A merit of the proposed method is discussed.

Paper Details

Date Published: 7 October 2014
PDF: 7 pages
Proc. SPIE 9249, Electro-Optical and Infrared Systems: Technology and Applications XI, 92490J (7 October 2014); doi: 10.1117/12.2067001
Show Author Affiliations
Takashi Toriu, Osaka City Univ. (Japan)
Thi Thi Zin, Univ. of Miyazaki (Japan)
Hiromitsu Hama, Osaka City Univ. (Japan)

Published in SPIE Proceedings Vol. 9249:
Electro-Optical and Infrared Systems: Technology and Applications XI
David A. Huckridge; Reinhard Ebert, Editor(s)

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