在导弹跟踪、无人机导航等目标跟踪任务的应用场景中,目标跟踪轨迹受到多种因素的影响,如光线变化、相似目标干扰与遮挡等。不可避免地会引入逐渐累积的动态误差而产生目标漂移的现象,这使得一般的基于孪生网络的跟踪器难以在长时跟踪中保持鲁棒性。为解决此问题,基于人类观察习惯和目标完整性特征设计了一种目标完整性修正模块,并将其与SiamRPN算法相结合,通过在预测过程中动态地给出预测框修正建议的方式来降低跟踪过程中的累计误差,提高模型抗漂移能力。在UAV123和OTB100等公开数据集上进行的对比实验表明目标完整性修正模块对于提升孪生网络目标跟踪算法的长时跟踪性能有明显作用,同时可以缓解光线变化、目标姿态及尺度变化、遮挡等各种干扰因素对跟踪器所施加的不利影响。

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.