Abstract: To achieve high real-time performance and high success rate of the target tracking algorithm in UAV applications, we propose an improved algorithm based on correlation filtering for tracking loss caused by target occlusion and target large deformation. The algorithm includes an adaptive position correction mechanism and a model update strategy. By extracting the histogram of the oriented gradient features of the target region, we train the filter and predict the target position of the next frame. When the position does not satisfy the high confidence condition, the fusion color naming features correct the position. To improve the efficiency of the algorithm, principal component analysis dimension reduction processing is performed on the merged features. The filter model is updated using average peak correlation energy, multi-peak detection, and maximum response value. In the experiment, we compare the improved algorithm with excellent algorithms that have been developed in recent years. Results show that the proposed algorithm has higher tracking accuracy in the scene where the target is occluded and target's large deformation.