Color Tracking by Transductive Learning

Abstract

One of the difficulties of color tracking is that color changes in different lighting conditions, and static color models would be inadequate to capture the nonstationary color distribution over time. Although some work has been done on adaptive color models, this problem still needs further investigation. Different from many other approaches, we formulate the nonstationary color tracking problem as a transductive learning problem, in which the generalization of a trained color classifier is only defined on the pixels in a specific image, rather than the whole color space. This formulation offers a way to design and transduce color classifiers through non-stationary color distribution. Instead of assuming a color transition model. We assume that some unlabeled pixels in a new image frame can be "confidently" labeled by a "weak classifier" according to a preset confidence level. The proposed Discriminant-EM (D-EM) algorithm offers an effective way to transduce color classifiers as well as automatically select a good color space. Experiments show that D-EM successfully handles some problems in color tracking. As a component our natural gesture interface, this algorithm gives tight bounding boxes of the hand or face regions in video sequences.