Motion estimation and segmentation using a global Bayesian approach

An approach to the problem of optic flow estimation and segmentation from image sequences is presented. It is shown that optic flow estimation and segmentation can be expressed, within a Bayesian decision framework, as a global estimation problem. The unknown process to be estimated corresponds to the 2D relative velocity field and to the motion boundaries. Several observations are used in the scheme, involving the spatiotemporal gradients of the image sequence and the output of an intensity edge detector. The unknown velocity field and motion discontinuities are modeled using a joint Markov random field, allowing the smoothing of the velocity field and the preservation of motion boundaries. Critical areas, such as occluding regions, are detected using a likelihood test and, in this case, a modified interaction model is applied. Results are presented on a real-world digital TV sequence involving complex 3D motions and occlusions