Figure-Ground Segmentation—Pixel-Based

Abstract

Background subtraction is a widely used concept to detect moving objects in videos taken from a static camera. In the last two decades several algorithms have been developed for background subtraction and were used in various important applications such as visual surveillance, sports video analysis, motion capture, etc.Various statistical approaches have been proposed to model scene background. In this chapter we review the concept and the practice in background subtraction. We discuss several basic statistical background subtraction models, including parametric Gaussian models and nonparametric models. We discuss the issue of shadow suppression, which is essential for human motion analysis applications. We also discuss approaches and tradeoffs for background maintenance. We also point out many of the recent developments in background subtraction paradigm.