Robust PCA-Based Visual Tracking by Adaptively Maximizing the Matching Residual Likelihood

A new similarity measure called matching residual likelihood (MRL) is presented for the task of visual tracking. MRL estimates the likelihood of the matching residual between the object representation model and the new candidate image based on previous matching errors. A posterior probability called a posterior matching residual probability is modeled based on the matching residual likelihood, the object motion model between sequential states, and the prior probability to estimate the density distribution of the object location. At every frame, an on-line algorithm is used to learn a low-dimensional PCA model from the object image. Then the object is located by maximizing the posterior matching residual probability distribution of the object state based on a robust factored sampling algorithm. The proposed method cab readily update the similarity measure to handle significant appearance changes while it is still robust to outliers and occlusion. In our experiments, the proposed tracker is applied on several challenging image sequences and the result is compared with other state-of-the-art methods and the ground truth data. The comparison results show the robustness and accuracy of our tracker in existence of large object motion and appearance variation, occlusion, outliers, and illumination changes.