Contour tracking in clutter: A subset approach

A new method for tracking contours of moving objects in clutter is presente d. For a given object, a model of its contours is learned from training dat a in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of conto urs separately. In the course of tracking, multiple contours may be observe d due to the presence of extraneous edges in the form of clutter; the learn ed model guides the algorithm in picking out the correct one. The algorithm , which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed alg orithm to the tracking of a flexing finger and to a conversing individual's lips are presented.