Robust appearance-based tracking using a sparse Bayesian classifier

Abstract

An appearance-based approach to track an object that may undergo appearance change is proposed. Unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. Through the use of a sparse Bayesian classifier, high classification and detection accuracy can be maintained even if a reduced feature vector is used. In addition, the classifier allows online-training which enables online-updating of the original classification model and provides better adaptability. Experiments show that the method can be used to track targets undergo appearance change due to the change in view-point, facial expression and lighting direction. © 2006 IEEE.