Robust Estimation for Background Subtraction

Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel Robust Estimators model and a background subtraction approach. The method could handle various small motions of background objects such as swaying tree branches and bushes. The model measures the central value and their variability, i.e. estimates the location and scale. These estimates should preferably be robust against outliers, as reflected by their small-sample breakdown values. The model adapts quickly to changes in the scene that enables very sensitive detection of moving targets. The results show that the proposed method is effective and efficient in real-time and accurate background maintenance in complex environment