Online background learning for illumination-robust foreground detection

This paper presents a background modeling algorithm and a foreground detecting method which is robust against illumination change, providing a novel and practical choice for intelligent video surveillance systems using static cameras. This paper first introduces an online Expectation Maximization algorithm which is developed from the basic batch edition to update the mixture models in real time. Then a spherical K-means clustering method is used to provide more accurate direction for the update of Gaussian Mixture Models after a deep study of RGB space features under illumination changes. Foreground detection is carried out using a statistical framework and RGB pixel intensity judgments. The results show the proposed algorithm outcompete several classic methods in efficiency, accuracy, and robustness to perturbations from illumination changes, on a sampling of problems.