Focusing on the target tracking drift and loss problems under severe occlusion for the traditional tracking methods, a long-term robust target tracking algorithm is proposed in the framework of Kernelized Correlation Filter (KCF) tracking. A combined confidence measurement method including occlusion information is introduced during the tracking process and used for the robust updates. If the result of the confidence graph by the KCF algorithm indicates that the target is occluded, a block mean shift (MS) algorithm is introduced to track this target and the local information is used to obtain the final location of this target. The performance of this algorithm is tested based on the eight sets of video sequences in the OTB-13 test library. The accuracy is increased by 0.7% and the success rate is increased by 5.7% compared with those of the traditional KCF algorithms. The test results show that even when the target is seriously occluded, the proposed algorithm still has a good tracking effect and a long-term stable target tracking is realized.