Considering the fast motion, illumination variation, and scale transform of tracking targets in actual scenarios, a triplet network based on a dynamic feature attention (DFA) model for object tracking is proposed to solve these problems. Specifically, on the basis of the SiamRPN++ tracking framework, an online update triplet network with dynamic template branches is designed to strengthen the semantic information of extracted features and improve the matching similarity between template features and search features. A sample generation method for the triplet network training is developed to change the allocation of negative samples and improve the balance of positive and negative training samples. Moreover, a DFA model, where the historical dynamic features of the templates are enhanced through equivalent self-attention and mutual attention operation, is designed to achieve the adaptive refinement of template features. Meanwhile, the channel attention score is used to control the weight distribution of the search feature maps, and the response of the score maps is improved. Compared with the state-of-the-art algorithms such as SiamRPN++ and SiamBAN, the proposed algorithm has achieved the highest success rate (71.0%) and the best robustness (0.122) on the OTB100 and VOT2018 datasets that contain scenes with motion blur, illumination variation, and similar background interference. This algorithm also can meet the requirement of real-time target tracking.