Abstract: In order to solve the problem of object tracking failure caused by object occlusion in object tracking algorithm based on a single feature, this paper proposes an object tracking algorithm based on adaptive feature fusion of color name(CN) feature and histogram of oriented gradient(HOG) feature. Firstly, a kernelized correlation filter method based on CN features and HOG features respectively is applied to predict the positions of the object. Then the final object location is obtained by assigning the filter response values as their corresponding weights for the two predicted positions, so that the accuracy of object location is improved. Next, the change rate of image frames by mean error of two adjacent frames is used to piece-wisely adjust the learning rate of the classifier. This strategy solves the problem of tracking failure caused by object occlusion. Finally, 9 groups of standard test video sequences are tested to verify the performance of the proposed algorithm. Compared with the best among the other three correlation filtering tracking algorithms, the average center location error is reduced by 16.78 pixels, the average distance precision is increased by 11.01% and the average overlapping precision is increased by 14.87%. The proposed tracking method is still able to track the moving object stably and accurately in the case of severe occlusion of the object.