Person re-identification (ReID), as a crucial technology in the field of security, plays an important role in security detection and people counting. Current security and monitoring systems largely rely on visual information, which may infringe on personal privacy and be susceptible to interference from pedestrian appearances and clothing in certain scenarios. Meanwhile, the widespread use of routers offers new possibilities for ReID. This letter introduces a method using WiFi Channel State Information (CSI), leveraging the multipath propagation characteristics of WiFi signals as a basis for distinguishing different pedestrian features. We propose a two-stream network structure capable of processing variable-length data, which analyzes the amplitude in the time domain and the phase in the frequency domain of WiFi signals, fuses time-frequency information through continuous lateral connections, and employs advanced objective functions for representation and metric learning. Tested on a dataset collected in the real world, our method achieves 93.68% mAP and 98.13% Rank-1.

翻译：行人重识别作为安防领域的关键技术，在安全检测与人员计数中发挥着重要作用。当前安防监控系统主要依赖视觉信息，这可能侵犯个人隐私，且在特定场景下易受行人外貌与服装的干扰。与此同时，路由器的广泛应用为行人重识别提供了新的可能性。本文提出一种利用WiFi信道状态信息的方法，通过WiFi信号的多径传播特性作为区分不同行人特征的依据。我们设计了一种能够处理变长数据的双流网络结构，该结构分别分析WiFi信号的时域幅度与频域相位，通过连续的横向连接融合时频信息，并采用先进的损失函数进行表征学习与度量学习。在真实场景采集的数据集上进行测试，本方法取得了93.68%的平均精度均值与98.13%的Rank-1准确率。