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I'm so stupid not to understand the comment from the author, so here my explanation.. We want to clustering pixel value at each coordinate (x,y) with mixture of Gaussian, we know that Gaussian distribution have 2 main parameter which is mean, and covariance/variance, and in background subtraction we classify the Gaussian distribution which is foreground or background first by sorting the distributions by weight/(covariance or variance) from the highest, and then sum the weight from the sorting Gaussian before which larger than some threshold (T). For example: we use RGB color space. As I told before, we need mean, covariance, and weight/covariance for each Gaussian distribution.. So we need 3 for save each mean (because it's RGB not grayscale), 3 for save each variance, and 1 for save each weight, 1 for save each weight/covariance ratio, so the total things we need to save for each Gaussian distribution =3+3+1+1=8 that's where the equation (2+2*nchannel) come from..