To deal with the limitation of boundary effect and improve tracking speed in the traditional object tracking based on correlation filter, a fast discriminative scale space with background-aware correlation filters (BACF)algorithm is proposed. The BACF algorithm effectively solves the boundary effect caused by cyclic shift, greatly increases the quality and quantity of training samples, and thus improves the performance of the tracker. However, because of its disadvantage in scale detection strategy, it seriously affects its tracking speed. The we design a one-dimensional scale filter. The scale filter and translation filter are trained and optimized independently. The proposed algorithm greatly improves tracking speed while ensuring scale and translation estimation. The experimental results show that compared with BACF algorithm, the proposed algorithm can improve the tracking speed by about 75% without losing the tracking accuracy.