BACKGROUND BASIS SELECTION FROM MULTIPLE CLUSTERING ON LOCAL NEIGHBORHOOD STRUCTURE

Foreground detection with dynamic background is a challenging task in video surveillance analysis. When clean background bases are constructed, regression based foreground detection usually becomes more effective. In this paper, a novel basis selection method based on local neighborhood structure is proposed. The present method first constructs local neighborhood relationships among the basis candidates in a reconstruction manner. Then a multiple clustering strategy is designed to evaluate these basis candidates on local neighborhood structure. According to the evaluation score given by multiple clustering process, clean background bases (including dynamic background) are separated from candidates corrupted by foreground. By adding the proposed basis selection process to a modified linear regression framework, the foreground detection can be implemented in a more effective way. Experimental results on multiple videos show that the modified framework with basis selection is competitive with the state of the art.