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Co-Tracking with a Manifold Anchor

In its original form, co-training is a process by which multiple learners can boost classification accuracy using unlabeled data points. While co-training has been applied to problems such as visual detection [20] and web page identification [6], this paper describes its use in an application that tracks heads in video. The inherent problems with this approach are discussed, and a geometric solution is proposed. By creating a manifold representing possible poses and using the distance from this manifold as a regularization term, tracking accuracy can be improved. A series of nearest neighbor algorithms are presented, and empirical results using this manifold anchor are discussed.