The problem with the background subtractor from the previous section is that the background will generally change due to illumination and other effects. Another fact is that the background may not be readily available, or the concept of background can change, for instance, when someone leaves a luggage in a video surveillance application. The luggage might be a foreground object for the first frames, but afterwards, it should be forgotten.

An interesting algorithm to deal with these problems uses the running average concept. Instead of always using the first frame as a clear background, it will update it constantly by calculating a moving average of it. Consider the following equation, which will be executed, updating each pixel from the old average and considering each pixel from the recently acquired image:

Note that is the new pixel value; is the value of the average background at time t-1, which would be the last frame; is the new value for the background...