Although we can get very good results with the previous idea, some more advanced methods have been proposed in literature. A great approach, proposed by Grimson in 1999, is to use not just one running average, but more averages so that if a pixel fluctuates between the two orbit points, these two running averages are calculated. If it does not fit any of them, it is considered foreground.
Besides, Grimson's approach also keeps the variance of the pixels, which is a measure of how far a set of numbers is spread out, taken from statistics. With a mean and a variance, a Gaussian model can be calculated and a probability can be measured to be taken into consideration, yielding a Mixture of Gaussians model (MOG). This can be very useful when branches and leaves are moving in the background.
Unfortunately, Grimson's method suffers from slow learning in the beginning and it can not distinguish between the moving shadows and moving objects. Therefore, an improved technique...
