In long-term tracking datasets, it is often encountered with complex scenarios where the object is occluded or temporarily leaves the field of view and then reappears. To cope with these challenges, short-term visual tracking algorithms are usually combined with re-detection methods to construct long-term visual tracking algorithms. However, the existing re-detection methods have limitations in tracking accuracy, which restricts the performance of long-term visual tracking algorithms to a certain extent. To address this problem, a global re-detection method based on feature interaction Siamese network is proposed. Firstly, the ResNet50 is used to extract multi-layer features from template images and search images. Secondly, by introducing the cross-scale feature interaction module, the deep feature interaction unit, and the shallow feature interaction unit, a richer and more comprehensive feature representation is obtained. Finally, the short-term tracking algorithm DiMP is combined with the proposed global re-detection method to form a long-term visual tracking algorithm. The long-term visual tracking algorithm is tested on several publicly available datasets, including UAV20L, LaSOT, UAV123 and VOT2020-LT. The experimental results show that the addition of the global re-detection method improves the accuracy and robustness of the long-term tracking algorithm.