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This paper presents a new adaptive background model for grayscale video sequences, that includes shadows and highlight detection. In the training period, statistics are computed for each image pixel to obtain the initial background model and an estimate of the image global noise, even in the presence of several moving objects. Each new frame is then compared to this background model, and spatio-temporal features are used to obtain foreground pixels. Local statistics are then used to detect shadows and highlights, and pixels that are detected as either shadow or highlight for a certain number of frames are adapted to become part of the background. Experimental results indicate that the proposed algorithm can effectively detect shadows and highlights, adapting the background with respect to illumination changes.