A robust boosting tracker with minimum error bound in a co-training framework

The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tracking, and some online and semi-supervised algorithms are developed to handle these difficulties. In this paper, we consider tracking as a classification problem and present a novel tracking method based on boosting in a co-training framework. The proposed tracker can be online updated and boosted with multi-view weak hypothesis. The most important contribution of this paper is that we find a boosting error upper bound in a co-training framework to guide the novel tracker construction. In theory, the proposed tracking method is proved to minimize this error bound. In experiments, the accuracy rate of foreground/ background classification and the tracking results are both served as evaluation metrics. Experimental results show good performance of proposed novel tracker on challenging sequences.