Visual Tracking via Subspace Learning: A Discriminative Approach

Good tracking performance is in general attributed to accurate representation over previously obtained targets and/or reliable discrimination between the target and the surrounding background. In this work, a robust tracker is proposed by integrating the advantages of both approaches. A subspace is constructed to represent the target and the neighboring background, and their class labels are propagated simultaneously via the learned subspace. In addition, a novel criterion is proposed, by taking account of both the reliability of discrimination and the accuracy of representation, to identify the target from numerous target candidates in each frame. Thus, the ambiguity in the class labels of neighboring background samples, which influences the reliability of the discriminative tracking model, is effectively alleviated, while the training set still remains small. Extensive experiments demonstrate that the proposed approach outperforms most state-of-the-art trackers.