We address the problem of tracking efficiently feature points along image sequences. To estimate the undergoing movement we use an approach based on Kalman filtering which performs the prediction and correction of the features movement in every image frame. In this paper measured data is incorporated by optimizing the global correspondence set based on efficient approximations of the Mahalanobis distances (MD). We analyze the difference between using the MD and its efficient approximation in the tracking results, and also examine the related computational costs. Experimental results which validate our approach are presented.