MOG does have some disadvantages. Backgrounds having fast variations are not easily modeled with just a few Gaussians accurately, and it may fail to provide sensitive detection (which is mentioned in [9]). In addition, depending on the learning rate to adapt to background changes, MOG faces trade-off problems. For a low learning rate, it produces a wide model that has difficulty in detecting a sudden change to the background. If the model adapts too quickly, slowly moving foreground pixels will be absorbed into the background model, resulting in a high false negative rate. This is the foreground aperture problem described in [10].