Focusing on the problem of robust object tracking

we propose an object-tracking algorithm based on dictionary learning and practical filter. We represent local image patches of a target object by codes with offline dictionary learning and pose object tracking as a binary classification problem.A learned classifier and a practical filter are used to estimate the tracking result. The proposed method can solve the difficulty caused by illumination variations

complex backgrounds

occlusions and so on.We do the simulations on a variety of challenging sequences. Experiments on different sequences with evaluation of the state-of-the art methods show our algorithm performs favorable performance.