Illumination invariant foreground detection using multi-subspace learning

Article type: Research Article

Authors: Dong, Y. | Han, T.X. | DeSouza, G.N.^{; *}

Affiliations: Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA

Correspondence: [*] Corresponding author. Tel.: +1 573 882 5579; Fax: +1 573 882 0397; E-mail: DeSouzaG@missouri.edu

Abstract: We propose a learning algorithm using multiple eigen subspaces to handle sudden illumination variations in background subtraction. The feature space is organized into clusters representing the different lighting conditions. A Local Principle Component Analysis (LPCA) transformation is used to learn a separate eigen subspace for each cluster. When a new image is presented, the system automatically selects a learned subspace that shares the closest lighting condition with the input image, which is then projected onto the subspace so the system can classify background and foreground pixels. The experimental results demonstrate that the proposed algorithm outperforms the original EigenBackground algorithm and Gaussian Mixture Model (GMM) especially under sudden illumination changes.

Keywords: Multi-feature subspace, LPCA, illumination invariance, eigenspace

DOI: 10.3233/KES-2010-0188

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 14, no. 1, pp. 31-41, 2010