Learning OpenCV, 2nd Edition

9

Background Subtraction

0BOverview of Background Subtraction

Because of its simplicity and because camera locations are fixed in many contexts, background subtraction

(aka background differencing) is a fundamental image processing operation for video security applications.

Toyama, Krumm, Brumitt, and Meyers give a good overview and comparison with 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. In that context, we present ...