Background initialization and foreground segmentation for bootstrapping video sequences

Abstract

In this study, an effective background initialization and foreground 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 four categories, namely, “background,”“still object,”“illumination change,” and “moving object.” Then, a new background updating scheme is developed, in which a side-match measure is used to determine whether the background is exposed. Finally, using the edge information, an improved noise removal and shadow suppression procedure with two morphological operations is adopted to enhance the final segmented foreground. Based on the experimental results obtained in this study, as compared with three comparison approaches, the proposed approach produces better background initialization and foreground segmentation results.