Moving object detection in video by detecting non-Gaussian regions in subbands and active contours

A multi-stage moving object detection algorithm in video is described in this paper. First, the camera motion is eliminated by motion compensation. An adaptive subband decomposition structure is then used to analyze the difference image. In the high-band subimages, moving objects which produce outliers are detected using a statistical test determining non-Gaussian regions. It turns out that the distribution of the subimage pixels is almost Gaussian in general. But, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. Regions containing moving objects in the original image frame are detected by detecting regions containing outliers in subimages. Finally, active contours are initiated in these regions in the wavelet domain and object boundaries are accurately estimated.