Illumination invariant foreground detection using multi-subspace learning

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.