Patent ID: 7536030

Claim:
A method comprising: representing a 3-dimensional (3D) tracking of a visual object in a video sequence using a computer and as a probabilistic graphical model which includes a dynamical Bayesian network, wherein the representing the 3D tracking includes establishing a 3D model of the visual object, wherein visual features of the visual object are represented by the 3D model points; inferring a current pose of the visual object in a current frame of the video sequence from the probabilistic graphical model based on a posteriors of pose states in previous frames of the video sequence using the computer; iteratively refining estimations associated with the first and second conditional distributions of a joint distribution of the dynamical Bayesian network using the computer; wherein the first conditional distribution comprises a distribution of a relative pose, given correspondences between the 3D model points and 2-dimensional (2D) features of the visual object; wherein the second conditional distribution comprises a distribution of matching features of the visual object between two frames of the video sequence, given the 3D model points and given a relative pose estimation associated with the first conditional distribution; and using a Bayesian fusion of the iteratively refined estimations using the computer to obtain the current pose of the visual object, wherein the iteratively refined estimations include an iteratively refined relative pose estimation and an iteratively refined feature matching estimation.