Abstract: A method is discussed of tracking objects in a series of n-D images (102) that have objects (106, 108) appearing in a background (110, 112), that method comprises using a probabilistic model of the appearance of the objects and of the appearance of the background in the images, and using an evaluation of whether particular pixels in the images (102) are a part of an object (106, 108) or a part of the background (110, 112), that evaluation comprising determining the posterior model probabilities that a particular pixel (x) or group of pixels belongs to an object or to the background, and further comprising marginalising over these object/background membership probabilities to yield a function of the pose parameters of the objects, where at least the object/background membership is adjudged to be a nuisance parameter and marginalised out.

Abstract: A method is discussed of tracking objects in a series of n-D images (102) that have objects (106, 108) appearing in a background (110, 112), that method comprises using a probabilistic model of the appearance of the objects and of the appearance of the background in the images, and using an evaluation of whether particular pixels in the images (102) are a part of an object (106, 108) or a part of the background (110, 112), that evaluation comprising determining the posterior model probabilities that a particular pixel (x) or group of pixels belongs to an object or to the background, and further comprising marginalising over these object/background membership probabilities to yield a function of the pose parameters of the objects, where at least the object/background membership is adjudged to be a nuisance parameter and marginalised out.