在监控系统中，行人经常被各种障碍物遮挡，为解决这一问题，提出了一种基于对抗防御网络的遮挡行人重识别方法，它将遮挡视为噪声干扰，并从对抗性防御的角度解决了这个问题。首先，分别引入了随机擦除干扰、随机变换干扰、随机噪声干扰3种方法来生成3种具有不完整信息的对抗性特征信息，以保护行人重识别系统免受信息缺失的干扰。其次，提出了多层级注意力机制，该模块通过更多地关注遮挡的身体部分或利用上下文信息来补偿遮挡带来的信息损失，从而提升模型在遮挡情况下的识别性能。最后再通过图像高低频特征增强模块，整合和强化被遮挡行人的高低频特征信息，更进一步地提升模型的识别性能。对本文方法进行了消融实验，在相关数据集上的实验结果证明了该方法的有效性。

In surveillance systems, pedestrians are often occluded by various obstacles. To address this issue, an occluded pedestrian re-identification method based on an adversarial defense network was proposed, which regards occlusion as noise interference and solves the problem from the perspective of adversarial defense. First, three methods: random erasure interference, random transformation interference, and random noise interference were introduced to generate three types of adversarial feature information with incomplete details, protecting the pedestrian re-identification system from the interference of information loss. Second, a multi-level attention mechanism was put forward. This module compensates for the information loss caused by occlusion by paying more attention to the occluded body parts or utilizing contextual information, thereby improving the recognition performance of the model in occluded scenarios. Finally, through the image high-low frequency feature enhancement module, the high-low frequency feature information of occluded pedestrians was integrated and strengthened, further boosting the model's recognition performance. Ablation experiments on the proposed method and experimental results on relevant datasets prove its effectiveness.