Motion estimation in the presence of occlusion is a wide open research field. As such, a critical component of the research is formulating a computational framework. A distinguishing aspect of our motion estimation scheme for solving the occlusion problem is that it cleanly handles the situation where the tracked features appear in different frames and, as features become unobservable, new features may need to be incorporated. As opposed to employing a 2D parametric motion model which restricts the object to a planar surface, we use a 3D motion model to capture the object motion and shape vectors. The precise 3D contour of the tracked object is fine-tuned within a predicted potential field.
['Jean Gao', 'Avinash C. Kak']
A multi-frame based motion estimation for semantic object tracking in the presence of occlusion