In application scenarios such as missile tracking and drone navigation, the target tracking trajectory is influenced by various factors, such as changes in lighting, interference from similar targets, and occlusion. This inevitably introduces dynamically accumulated errors, leading to the phenomenon of target drift, making it challenging for conventional Siamese network-based trackers to maintain both accuracy and robustness in long-term tracking scenarios. To overcome this issue, a novel object integrity correction module is proposed, which takes inspiration from human visual habits and the intrinsic features of object integrity. This module is integrated with the SiamRPN algorithm, allowing for dynamic adjustment of the predicted bounding box during the tracking process. By providing real-time correction suggestions, this method effectively reduces the accumulation of errors that typically occurs during tracking, thereby enhancing the model’s ability to resist drift and maintain accuracy over extended periods. The effectiveness of the object integrity correction module is validated through extensive comparative experiments on publicly available datasets such as UAV123 and OTB100. These experiments demonstrate that the proposed module significantly improves the long-term tracking performance of Siamese network-based object tracking algorithms. Moreover, it alleviates the negative impact of various challenging factors, including illumination changes, variations in object pose and scale, as well as occlusion. The results show that the proposed method not only enhances robustness but also enables more reliable tracking in real-world, dynamic environments.