The conventional correlation filtering methods are known to demonstrate poor tracking stability for fast moving and fast deforming targets. Therefore, this paper proposes a correlation filter tracking algorithm for adaptive feature selection. First, the basic features are extracted in the candidate regions using a position filter and a color probability model and fused in different weight combinations to obtain multiple fusion features. Then, the credibility of the fusion features is determined and the features with relatively high credibility are selected as the tracking features of the current frame to estimate the candidate position of the target. Finally, if the maximum credibility is less than the credibility threshold, the detector is activated to redetect the target position; otherwise, the candidate position is just the final position. Meanwhile, the target model is updated to ensure the accuracy of target description. The experimental results on the standard OTB50 and OTB100 datasets show that the proposed tracking method has relatively high tracking accuracy and good robustness under the conditions of motion blurring, illumination variation, and fast motion.