Background model initialization in moving object detection with shadow elimination

Background subtraction methods are widely exploited for moving object detection in many applications. How to correctly and efficiently model the background is the key technique to such approaches. We review the typical systems characterized by pixel-wise multiple Gaussians statistics. Our research on model initialization is introduced afterwards, where a fast online initialization algorithm is devised to train the model quickly and correctly. Then a convenient way to combine luminance distortion with chrominance distortion is proposed for shadow detection in complex scene. Finally, relevant experimental results are provided to highlight our methods.