Aiming at the problem of tracking failure caused by object occlusion and out of view in object tracking, a two-step correlation filter for object tracking algorithm was proposed to advance the robustness of the object tracking via correlation filter. Firstly, according to the characteristics of different cell size of histogram of oriented gradient (HOG) feature, a two-step correlation filter object tracking framework was presented, which can improve the tracking accuracy and simultaneously ensure the tracking speed. Then, fusing multiple object features and obtaining more characteristic representation comprehensively were to promote the robustness of object tracking. Finally, an object template adaptive updating strategy was proposed based on the object tracking confidence index, which was to solve the problem that the object template was contaminated when the target was occluded. The experiment was validated on the OTB100 standard object tracking dataset. The results of comparison with other tracking algorithms show that the tracking accuracy is improved by 6.0% and the tracking success rate is increased by 5.5% in comparison with the optimal tracking algorithm, and the average tracking speed is 27.4 fps, which ensures the real-time performance of object tracking. In the application of actual object tracking, the algorithm can still track the target stably and accurately in the case of severe occlusion.