There is a long time detection problem caused by too large surveyed area in the classical Tracking-Learning-Detection (TLD) target tracking algorithm. Moreover, the TLD algorithm could not do the similar targets processing well. So in this paper, an efficient approach called TLD-DO was proposed for tracking targets in which the surveyed areas could be automatically adjusted according to the target's velocity of movement. In order to accelerate the process speed of TLD algorithm without reducing tracking precision, a novel algorithm named Double Kalman Filter (DKF) with optimal surveyed area which could reduce the detection range of TLD detector was constructed based on twice Kalman filtering operation for acceleration correction. Meanwhile, the improved method could also increase the accuracy of target tracking through eliminating the interference of the similar targets in complex background. The experimental results show that tracking effect of improved method is better than that of the original TLD algorithm under the circumstance of similar target disturbance. Furthermore, the detection speed has been improved 1.31-3.19 times for different videos and scenes. In addition, the improved method is robust to target vibration or distortion.