A Subset Approach to Contour Tracking in Clutter

A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data 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 contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned 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 algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Daniel Freedman, Michael S. Brandstein
• Keywords: Computer Vision, Contour Space, ICCV 1999, Model, Multiple Contours