In order to improve both the tracking accuracy and speed of the efficient convolution operators based tracking algorithm fusing the histogram of oriented gradient and color names features (ECO-HC), a weighted correlation filtering algorithm based on context and relocation is proposed. Considering the differences between the histogram of oriented gradient and color names features, the responses of two features are fused in different weights. The adaptive iterative method is used to predict the position of a target, which combines with the multi-scale search area, the contextual features and the relocation method when the target prediction is failure to further improve the tracking accuracy. The algorithm is evaluated on the OTB-100 dataset. The experimental results show that the average distance accuracy of the proposed algorithm is 89.2% and the average overlap rate is 80.6%, 3.6% and 2.1% higher than those of the ECO-HC method, respectively. In addition, the tracking speed on the central processing unit is 65.2 frame/s, superior to that of the other tracking algorithms compared in the experiments. The proposed algorithm effectively improves the tracking accuracy and can track the objects well under the condition of severe occlusion, illumination variation and other interferences.