A block-wise frame difference method for real-time video motion detection

This article proposes a motion detection method for real-time video analysis. It is the fundamental principle that the parts of the moving objects and the local changes of the images captured by static cameras are strongly correlated. Peak signal-to-noise ratio calculated in a block can characterize the significance of the changes in this area. Moving objects can therefore be detected by thresholding the peak signal-to-noise ratio of the blocks between two adjacent frames. The block-wise scheme used in this frame difference method can explore the local correlation of the movement in both space and time domains. This approach is robust to analyze the video images with noise and high variance caused by environmental changes, such as illuminations changes. Compared with other methods, the proposed method can achieve relatively high detection accuracy with less computation time, where real-time motion detection is available. Experimental results show that the proposed method cost averagely 50% of the running time of ViBe with 3.5% increase of the F-score on detection accuracy.