A Background Basis Selection-Based Foreground Detection Method

Foreground detection plays a fundamental role in video analysis. Frames with only background information are usually beneficial for many foreground detection algorithms, especially for regression-based methods where the background is recovered from a background basis matrix. However, many regression-based methods ignore the basis selection process or select bases by simple sampling, which may limit their performance. In this paper, a regression-based foreground detection method with a novel background basis selection process is proposed. The proposed basis selection method, which includes basis matrix construction and basis matrix update processes, aims to build an effective background basis matrix which helps to boost the performance of our foreground detection method. In our algorithm, the basis matrix construction process first builds the basis matrix locally with a multiple clustering evaluation process. With the locally constructed basis matrix, a modified linear regression-based foreground detection method is proposed for separating foreground and background globally. To further increase the representativeness and the adaptiveness of the background basis matrix, a basis matrix update algorithm is designed to incrementally replace the ineffective bases with new selected ones. Extensive experiments on challenging sequences demonstrate the effectiveness and the advantages of our method.