2 S. Derin Babacan 2006 Department of Electrical Engineering and Computer Science Evanston Northwestern University 85 Two fundamental tasks in computer vision and video processing applications are detection of foreground objects by subtracting the background and video segmentation. In this thesis we first present an overview of the approaches in the literature that are proposed to solve these challenging problems. This is followed by presentation of two novel spatiotemporal algorithms applicable to these problems with a stationary single camera systems. In the first part, we present a novel probabilistic background modeling and subtraction method that exploits spatial dependencies between pixels. By using an initial segmentation of the background scene, we model each pixel by a mixture of spatiotemporal Gaussian distributions. Using the ideas of the background model, we present a novel algorithm for segmenting video sequences into objects with smooth surfaces. The segmentation of image planes in the video is modeled as a spatial Gibbs-Markov random field and extension to 3D is accomplished by the temporal labeling algorithm. Experimental results for indoor and outdoor surveillance videos demonstrate the performance advantages of the proposed methods. Possible extensions and applications are outlined and conclusions are drawn.