The most existing algorithms have to build the complex model and draw a large number of training samples to achieve accurate object tracking,which will produce large amount of calculation. The proposed problem is not conducive to real-time tracking. In order to solve the problem, a real-time tracking method based on multi-channel kernel correlation filter was presented. Firstly, the target information of video frames were trained by using the nucleation ridge regression method to get the filter template. Secondly, the filter template was utilized to carry out the correlation measure for the possible area of the frame to be detected. Finally, the most relevant location was considered as the tracking result and the independent inputs of multiple channels were weighted and then added to solve the problem of multi-channel input. A large number of comparison experiments with the existing tracking methods show that, the proposed method guarantees the tracking accuracy and its tracking speed also has obvious advantages under different challenge factors. The proposed method avoids to extract a large number of samples by the correlation filter and use the dot product of frequency domain to replace the correlation operation of time-domain, which greatly reduces the computational complexity and makes the tracking speed completely meet the tracking demand of real-time scenario.