Abstract: The existing research on visual target tracking mainly focuses on the improvement of tracking performance. The amount of computation is generally too large to run in real-time on embedded computing platforms with limited resources, which seriously affects the practical application of tracking algorithms. This paper analyzes the existing tracking algorithms, and proposes an improved high-speed kernelized correlation tracking algorithm. On the one hand, the linear kernel function is used to solve the problem of a large amount of kernel function calculation in the correlation operation, on the other hand, the algorithm flow is optimized, and multiple Fourier transform calculations are placed in the algorithm initialization stage, so as to avoid the large amount of Fourier transform calculation in the tracking process. Combining the above measures, the original tracking main cycle needs to calculate ten times Fourier transform (FFT) to three times FFT. And through quantitative experimental analysis and verification, the speed of the proposed algorithm is increased to 4-5 times that of the original tracking algorithm, while the tracking performance is basically unchanged. The proposed method in this paper dramatically reduces the computational complexity of high-performance tracking algorithms and has a good application prospect on embedded computing platforms with limited computing performance.