Unsupervised background reconstruction based on iterative median blending and spatial segmentation

We propose in this paper a novel iterative approach for unsupervised reconstruction of static background from a complex video shot. After aligning some key-frames of the video onto a reference plane in order to compensate the camera motion, the basic idea of the suggested scheme is to iteratively reconstruct a precise image of the background using median blending and spatial segmentation. In each iteration, coarse binary masks, representing foreground moving objects, are estimated by comparing each motion-compensated key-frame with the corresponding part in the input background image. These masks are then refined by spatial segmentation while profiting of the semantic information offered by region maps. The iterative process allows the blending operator to eliminate the detected moving objects while reconstructing the output background image. Several experiments have been carried out to prove the effectiveness of the suggested unsupervised approach for precise background reconstruction of complex dynamic scenes after a relatively small number of iterations.