Visual object tracking algorithms based on Siamese networks yield promising results through offline training on large benchmarks. However, they cannot adapt well to changes in the target’s appearance and tracking scenarios during online tracking because they rely on a single initial template. Most existing template update algorithms use the tracking result of the previous frame to update the template. If the tracking results deteriorate, it can cause the tracker to accumulate incorrect templates, leading to tracking drift. In this work, we proposed a dynamic template updating Siamese network called dynamic template updating (DTU)-Track that utilizes status feedback with quality evaluation for visual object tracking. This network dynamically selects the tracking template for the next frame based on feedback from the tracking result of the previous frame. It calculates the updating quality score and tracking quality score by using the tracking result of the previous frame. The updating quality score determines whether the current tracking result can be stored in the template library. The tracking quality score quantifies the quality of the tracking status of the previous frame. The quality conversion module determines the number of templates required for the next frame by evaluating the tracking quality of the previous frame. The template extraction mechanism selects high-quality, diverse templates from the template library, thereby enabling the tracker to effectively adapt to changes in tracking scenarios and target’s appearance in the subsequent frame. Extensive experiments on large-scale benchmarks, such as OTB-2015, UAV123, GOT-10k, TrackingNet, and LaSOT, demonstrated our method’s superiority over other algorithms in various tracking scenarios, yielding a real-time speed of 36 FPS.