Background Subtraction Techniques Evaluation based on ∑-∆ Algorithm for Motion Detection

Processing a video stream to segment moving objects (foreground) from the static scene (background) is a critical first step in many computer vision applications. One of the common methods is using background subtraction approach, which detects moving objects by comparing each frame with the obtained background frame. In this paper, we examine background subtraction algorithm based on sigma-delta filter. This algorithm provides a simple and very fast approximation of the median and has the advantage of having low memory requirements. The interest of this method lies in the robustness provided by the non-linearity compared to the linear recursive average, and in the very low computational cost. However in the basic sigma-delta algorithm, the background model quickly degrades in complex urban scenes because it is easily “contaminated” by slow-moving or temporarily stopped objects. And in this algorithm ghost effect and aperture effect is clearly visible. This paper is a review to this algorithm and various approaches and improvements proposed for it. In this paper, first basic sigma-delta and then its important approaches is described. The purpose of this approaches and improvements is to eliminate or reduce the defects and disadvantages of the main algorithm. In the end, a quantitative comparison between these algorithms is carried out and improvements and advantage and disadvantages of each algorithm are evaluated.