Robust object tracking based on weighted subspace reconstruction error with forward: backward tracking criterion

It is a challenging task to develop an effective and robust object tracking method due to factors such as severe occlusion, background clutters, abrupt motion, illumination variation and so on. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. The discriminative weights are defined through minimizing reconstruction error with positive dictionary while maximizing reconstruction error with negative dictionary. Then, confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, the new evaluation method based on forward-backward tracking criterion to verify the robustness of the current tracking performance in updating stage, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against eleven state-of-the-art methods in terms of accuracy and robustness.