A fast motion segmentation algorithm based on hypothesis test for surveillance video coding

Motion segmentation is an important task in video comprehension and object based video coding. This paper proposes a fast motion segmentation algorithm based on hypothesis test. At first, statistical model of camera noise is obtained offline. Then, pixels are classified into the moving and still by hypothesis test and a binary mask image is generated. Median filtering is used further to remove isolated spots. At last, macro block (MB) mask is formed according to the number of moving pixels inside MBs. Experimental results show the proposed “test for pixels - median filtering - MB mask” strategy is robust without increasing complexity.