Using fast fourier transformation method with densely sampled image data opened a new chapter in visual object tracking area. It not only is capable of estimating optimal correlation filter with high certainty but also is superior in computational complexity compared to exhaustive convolution method. Nevertheless when appearance of the target object radically changes, the iteratively updated correlation filter may fail to compute plausible correlation of the object. Concerning this, we propose a method that monitors the motion of the object. Exploiting this information, correlation response map is compensated thus the tracker is able to locate the object with less error. The proposed method is verified by using 2014 VOT challenge benchmark dataset.