Tuned basis function (TBF) is a powerful technique for classification of two classes by transforming them into a new space, where both classes will have complementary eigenvectors. A target discrimination technique can be described based on these complementary eigenvector analyses under two classes: (1) target and (2) background clutter, where basis functions that best represent the desired targets form one class while the complementary basis functions form the second class. Since the TBF does not require pixel-based preprocessing, it provides significant advantages for target tracking applications. Furthermore, efficient eigenvector selection and subframe segmentation significantly reduce the computation burden of the target tracking algorithm. The performance of the proposed TBF-based target tracking algorithm has been tested using real-world forward looking infrared video sequences.