To improve the tracking precision and processing speed of the Tracking-Learning-Detection(TLD) algorithm under a complex environment, an efficient TLD target tracking algorithm based on BInary Normed Gradient(BING) algorithm was proposed. The local tracker failure predicting method based on spatial-temporal context and the global motion model estimation algorithm was introduced into the tracker to improve its precision and robustness. Then, the BING algorithm was used to replace a sliding window for searching the target to detect the candidate target by combining with a cascaded classifier, so that to reduce the search space and improve the processing speed of the detector. The sample weight was integrated into the online learning procedure to improve the accuracy of the classifier and to alleviate the drift to some extents. The experimental results on variant sequences demonstrate that the accurate rate and the frame rate of the improved TLD are 85% and 19.79 frame/s, respectively. Compared with original TLD and state-of-the-art tracking algorithm under the complex environment, the improved TLD has the superior performance on robustness, tracking precision and tracking speeds.