Learning OpenCV

Background Subtraction

Because of its simplicity and because camera locations are fixed in many contexts, background subtraction (aka background differencing) is probably the most fundamental image processing operation for video security applications. Toyama, Krumm, Brumitt, and Meyers give a good overview and comparison of many techniques [Toyama99]. In order to perform background subtraction, we first must "learn" a model of the background.

Once learned, this background model is compared against the current image and then the known background parts are subtracted away. The objects left after subtraction are presumably new foreground objects.

Of course "background" is an ill-defined concept that varies by application. For example, if you are watching a highway, perhaps average traffic flow should be considered background. Normally, background is considered to be any static or periodically moving parts of a scene that remain static or periodic over the period of interest. The whole ensemble may have time-varying components, such as trees waving in morning and evening wind but standing still at noon. Two common but substantially distinct environment categories that are likely to be encountered are indoor and outdoor scenes. We are interested in tools that will help us in both of these environments. First we will discuss the weaknesses of typical background models and then will move on to discuss higher-level scene models. Next we present a quick method that is mostly good for indoor ...