Aiming at the tracking failure caused by scale variation and single feature in the traditional kernel correlation filter tracking algorithm, the scale-adaptive correlation filter tracking based on multiple features is proposed. Firstly, in the position prediction stage, the characteristics of Hue feature and histogram of oriented gradient are analyzed, and the target location is predicted by distributing the weight of the output response values of the two features and each translation filter. Then, in the scale prediction stage, a scale filter is trained independently by the multi-scale image sampling in the target position, and the scale of the target is estimated according to the scale filter response value of the sample, which enables the tracking algorithm to adapt to the scale change of the target. Finally, the difference between two frames is used to adjust the learning rate adaptively to update the model of the translation filter. The experimental results demonstrate that this algorithm is higher than the traditional kernel correlation filter tracking algorithm by 25% in the tracking success rate and by 13% in the tracking precision. Furthermore, the improved tracking algorithm is still able to track the moving targets stably and accurately in the case of scale variation of the targets.