Over the last two decades, background modeling techniques have focused on representing the general appearance of a background that is assumed to be predominantly static. However, there are many situations in which there are active, moving elements that are effectively part of the background. Examples include tools in manipulative tasks or work settings where a small, fixed set of people are moving about. Such situations are not well modeled by traditional methods. In this paper, we present a background modeling approach, Actors on a Stage (AOS), that is able to accommodate both passive and active backgrounds. AOS is presented as a general, recursive estimation scheme for a background model. In this model, actors are a latent variable that is used to explain both occlusion of, and abrupt changes to, a background model. We demonstrate AOS in two different situations: a person writing on a blackboard, and a microretinal membrane peel. Additionally, we show how our method performs compared to traditional techniques in these settings, and on standard image sequences.