Abstract: Traditional PTZ (pan-tilt-zoom) camera control methods rely on manual operation. By those methods, dynamic objects can't be tracked continuously in real time, and the accuracy of object tracking is low. To solve those problems, a PTZ camera control method based on an improved KCF (kernelized correlation filter) object tracking algorithm is proposed. Firstly, the traditional KCF object tracking algorithm is improved in terms of motion state estimation and scale estimation. In target motion state estimation, the particle filter framework is combined with the traditional KCF algorithm to estimate the position of the moving target. With the motion state estimation method based on probability, stabler target signals can be obtained, and background interference information is reduced, thereby stronger anti-jamming ability is accomplished in complex scenarios. In target scale estimation, correlation filter is applied to estimating the scale of the target in scale pyramid, which improves the algorithm adaptability to the scale changes of moving targets. Secondly, the PTZ camera is controlled with the PELCO_D protocol according to the information of tracking results in order to keep the target within the viewfinder. Finally, the comparison experiments are carried out between the improved KCF algorithm and the other tracking algorithms using the Benchmark data sets in order to verify the effectiveness and robustness of the improved algorithm. The algorithm is applied to the PTZ camera control, and the PC system of PTZ camera is controlled by the improved KCF algorithm with C++ language. The experiment results show that the PTZ camera control method can track the obscured target accurately and keep it in the viewfinder stably.