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A revaluation of frame difference in fast and robust motion detection

In this paper we propose a robust approach to detect moving objects for video surveillance applications. We demonstrate that a jointly use of frame by frame difference with a background subtraction algorithm allows us to have a strong and fast pixel foreground classification without the need of previous background learning. The Joint Difference algorithm uses frame difference information to correct pixels classification made by a background subtraction algorithm while selectively updating the background model according to such classification. In this way we should perform motion segmentation also in presence of environmental changes such as illumination variations or "waking persons". The algorithm is capable of 15 fps tracking of moving people on 640-480 unsampled color images; results on both VSSN06 and Wallflower [8] benchmark videos are presented.