Background subtraction based on superpixels under multi-scale in complex scenes

Abstract

Background subtraction in complex scenes is a challenging problem of computer vision. Most existing algorithms analyze the variation in pixels or regions for background subtraction. Unfortunately, these works ignoring the neighborhood information or similarity among pixels and do not work well in complex scenes. To solve this problem, a novel background subtraction method based on SuperPixels under Multi-Scale (SPMS) is proposed. In SPMS, the foreground consists of superpixels with foreground or background label, which decided by the statistic of its variation. The variation in superpixels is robust to noise and environmental changes, which endows the SPMS with the ability to work in extreme environment such as adverse weather and dynamic scenes. Finally, the summary of foregrounds under multiple scales improve the accuracy of the proposed approach. The experiments on standard benchmarks demonstrate encouraging performance of the proposed approach in comparison with several state-of-the-art algorithms.