An effective foreground/background segmentation approach for bootstrapping video sequences

In this study, an effective foreground/background segmentation approach for bootstrapping video sequences is proposed. First, a modified block representation approach is used to classify each block of the current video frame into one of the four categories, namely, "background," "still object," "illumination change," and "moving object." Then, a new background updating scheme is developed, in which the side-match measure is used to determine whether the background exposes. Finally, an improved noise removal and shadow suppression procedure using the edge information is used to enhance the final segmented foreground. Based on the experimental results obtained in this study, as compared with two comparison approaches, the proposed approach has better background modeling and foreground extraction results.