Accurate Background Modeling for Moving Object Detection in a Dynamic Scene

Fast and accurate foreground detection in video sequences is the first step in many computer vision applications. In this paper, we propose a new method for background modeling that operates in color and gray spaces and that manages the entropy information to obtain the pixel state card. Our method is recursive and does not require a training period to handle various problems when classify pixels into either foreground or background. First, it starts by analyzing the pixel state card to build a dynamic matrix. This latter is used to selectively update background model. Secondly, our method eliminates noise and holes from the moving areas, removes uninteresting moving regions and refines the shape of foregrounds. A comparative study through quantitative and qualitative evaluations shows that our method can detect foreground efficiently and accurately in videos even in the presence of various problems including sudden and gradual illumination changes, shaking camera, background component changes, ghost, and foreground speed.