Abstract: Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The background subtraction methods apply probabilistic models to background intensities evolving in time nonparametric and mixture-of Gaussians. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this we adapt threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity we also apply a Markov model to change labels to improve spatial coherence of the detections, the proposed methodology is applicable to other background models as well. The strength of the scheme lies in its simplicity and the fact that it defines an intensity range for each pixel location in the background to accommodate illumination variation as well as motion in the background. The efficacy of the scheme is shown through comparative analysis with competitive methods. Both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance

Keywords: Background subtraction, tracking, object detection, surveillance.