Gaussian mixture classification for moving object detection in video surveillance environment

The paper deals with detection of moving objects by modelling pixel grey level distribution along the time. The detection of moving objects is based on learning and update of background pixel distributions. The choice of appropriate mixture's component for a given pixel is performed by likelihood maximization. An original Markov regularization is proposed to smooth detection. The method performs in real time on CIF resolution video and low cost commercial hardware.

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