To solve the insufficient tracking capability problem for a fully convolutional Siamese network (SiamFC) in complex scenarios such as those involving fast motion and large similar interference, SINT was introduced as a redetection network to improve the SiamFC. When multiple peaks appeared in the tracking response map, the proposed algorithm enabled the redetection network to redetermine the target position with higher accuracy. At the same time, a generative model was adopted to construct a template to adapt to various appearance changes of the target, and a high-confidence model update strategy was used to avoid the model corruption problem. Our algorithm is tested on OTB2013, and nine state-of-the-art algorithms are selected for comparison. The tracking accuracy of our algorithm reaches 88.8%, the best among all the algorithms selectes for comparison, and the success rate reaches 63.2%, which is the second best. Both these properties offer considerable improvement over the SiamFC results. Analysis of several representative video sequences demonstrate that our algorithm has high accuracy and strong robustness in cases involving fast motion, severe occlusion, background clutter, illumination changes, and long-term tracking.