The emergence of kernelized correlation filter has pushed the traditional target tracking algorithm to a new height. With its high speed, high precision, and high robustness, it has quickly gained wide recognition and attention from the society. In view of the shortcomings of the kernelized correlation filter in scale change, based on the relative position of the main features of the target when the scale changes and fast moving, the size of the target can be reflected. The block detection method is used to calculate the scale of the target. In respect, the kernelized correlation filter loses the target when dealing with the target with high-speed changes of the feature, and the target template is updated in advance by Kalman filter prediction to solve the problem of template update lag. Experimental results show that the proposed algorithm can improve the tracking accuracy steadily.