In order to effectively improve the accuracy and real-time performance of target tracking, a dual-model adaptive correlation filtering tracking algorithm based on multi-template matching is designed in this study. The parameters of the multi-template matching model and the kernel-related filtering tracking model are initialized firstly. Among them, the multi-template matching model takes the score function as the matching criterion between the template and the candidate sample, obtains the best target through the candidate sample score, and realizes the multi-template matching through the deformation and diversity similarity after updating the multi-template. The kernel correlation filter tracking model uses the collected target sample data to establish a circulant matrix, obtains the kernel correlation filter and the response confidence map by training the core ridge regression classifier. Then the target position of the next frame of image is obtained by using the response confidence map. An adaptive fusion strategy is adopted to obtain the estimated target position of the two models, and then the pyramid scale estimation strategy is employed to estimate the target scale change. Accurate target tracking is achieved by continuously updating each model parameter. The experimental results show that the center tracking error of the algorithm is lower than 15 dpi, the average tracking accuracy is higher than 98%, and the target positioning time is less than 100 ms in complex environments such as target occlusion or rotation and illumination changes. The above results indicate that the algorithm bears obvious application advantages in tracking accuracy and real-time performance.