Correlation filtering target tracking algorithm is based on the correlation between the target template and the image to be tested to realize the location and tracking of the target. Especially, when the kernelized correlation filters (KCF) is proposed which is faster and more accurate, the correlation filtering target tracking algorithm is pushed to a new height. However, with in-depth study of the KCF algorithm, it is found that the correlation filter has some serious shortcomings in the anti-occlusion performance. Especially in the case of a short-term disappearance of the target, it is extremely easy to lost the target. In order to solve this problem, a method combining KCF with the forward and backward error detecting algorithm is proposed. It detects the occlusion phenomenon by the forward-backward error algorithm, and retains the original target template in time after the occlusion occurs. Finally, it re-locates the position of the target by combing the prediction in a small range with the original template. Experimental results show that this method can effectively solve the occlusion condition when the target disappears completely and perform effective tracking after the target reappears.