Enhanced background subtraction using global motion compensation and mosaicing

Background subtraction is a widely used technique for video object segmentation. Its main drawback is its constraint to video from a static camera. Several proposals have been made to extend background model generation to camera movement, while few approaches can cope with many degrees of freedom in camera motion. We present a method to generate background images for unconstrained camera motion, zoom, rotation and even (weak) lens distortion. Our method is based on global motion estimation and a weighted summation of motion compensated images. The original contribution of our work is a statistical model that describes the deviation of local motion from global motion by a Rayleigh distribution. This allows to estimate background images where all regions that move different to global motion are suppressed, i.e. they are replaced by the appropriate background region from other frames. A quantitative evaluation on publicly available video-data shows the validity of our approach.