Online selection of discriminative features using Bayes error rate for visual tracking

Abstract

Online feature selection using Bayes error rate is proposed to address visual tracking problem, where the appearances of the tracked object and scene background change during tracking. Given likelihood functions of the object and background with respect to a feature, Bayes error rate is a natural way to evaluate discriminating power of the feature. From previous frame, object and background pixels are sampled to estimate likelihood functions of each color feature in the form of histogram. Then, all features are ranked according to their Bayes error rate. And the top N features with the smallest Bayes error rate are selected to generate a weight map for current frame, where mean shift is employed to find the local mode, and hence, the location of the object. Experimental results on real image sequences demonstrate the effectiveness of the proposed approach. © Springer-Verlag Berlin Heidelberg 2006.