Despite the considerable advances that are emerged in correlation filter-based tracking, in fact, they may achieve excellent performance in robustness, speed, and accuracy; they still fail when dealing with large-scale alteration and show the inability to handle long-term tracking in complex scenarios, where the object undergoes partial occlusion, out-of-view, and deformation. In this paper, we propose a robust approach to address two important problems: the first one is scale estimation in kernelized correlation filter (KCF), and the second one is how to update the model in the process of tracking. We aim in this work to overcome the scale fixed size limitation of kernelized correlation filter-based tracking algorithms and improve the mechanism of model online training. Our approach learns a separate correlation filter to estimate the accurate target scale by finding the scale's candidate that maximizes the output response of the correlation filter mentioned above. Besides, we define a minimum rate of similarity for the online model update to avoid training with failure detections. Our approach is evaluated in terms of precision and accuracy, on a commonly used tracking benchmark with 100 challenging videos; the experimental results show that our proposed tracker outperforms the KCF algorithm and shows promising performance compared to state-of-the-art tracking methods.