When modeling an object in a visual input image, a model likelihood function for generating a predicted model based on the input data is moderated as a function of the portion of the object so as to less penalize regions of the model that tend to be more noisy. For instance, background subtraction algorithms often do not accurately interpret shadows of objects because the characteristics of shadows depend on so many factors, such as cloudiness, angle of the sun's rays, topography of the background upon which the shadow is cast, etc. Accordingly, the model likelihood function applied to a predicted shadow region of the model will be moderated so as to less penalize differences between the model and the input image with respect to shadows then with respect to other more reliably predictable portions of the image. A model likelihood function additionally or alternately may be moderated as a function of the object classification in situations where it is known that certain object classifications are more reliable than others.
METHOD AND APPARATUS FOR PREDICTING THE ACCURACY OF A VIRTUAL SCENE BASED ON INCOMPLETE INFORMATION IN VIDEO