Video-based detection of moving and foreground objects is a key computer vision task. Temporal differencing of video frames is often used to detect objects in motion, but fails to detect slowmoving (relative to the video frame rate) or stationary objects. Adaptive background estimation is an alternative to temporal frame differencing that relies on building and maintaining statistical models describing background pixel behavior; however, it requires careful tuning of a learning rate parameter that controls the rate at which the model is updated. We propose an algorithm for statistical background modeling that selectively updates the model based on the previously detected foreground. We demonstrate empirically that the proposed approach is less sensitive to the choice of learning rate, thus enabling support for an extended range of object motion speeds, and at the same time being able to quickly adapt to fast changes in the appearance of the scene.