In this study, we propose a scale-adaptive correlation filter tracking algorithm based on the fusion of multiple features to handle the problems that the single feature of the kernel correlation filtering algorithm cannot adapt to the complex scenes observed during the tracking process and that the kernel correlation filtering algorithm cannot handle the scale changes of the target. First, under the framework of the correlation filtering algorithm, the fast histogram of oriented gradient and local binary pattern features are weighted adaptively based on the reliability of the feature response graph for localizing the target. Second, the scale estimation process estimates the scale of target using a scale pyramid to ensure good adaptability with respect to the target with scale change. The proposed algorithm and five other tracking methods are verified by testing on the OTB-50 dataset. Apart from outperforming the existing methods in terms of the accuracy and success rates, the proposed algorithm exhibits good robustness and a stable tracking performance.