用于行人重识别的可变长度WiFi CSI信号时频分析
Time-Frequency Analysis of Variable-Length WiFi CSI Signals for Person Re-Identification
AI总结：
针对视觉行人重识别存在隐私泄露、易受外貌衣着干扰的问题，提出基于WiFi CSI的双流网络，通过时频信息融合与先进目标函数实现可变长度信号处理，在真实数据集上取得93.68% mAP和98.13% Rank-1的优异性能。
摘要：
行人重识别（ReID）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。当前的安防监控系统主要依赖视觉信息，这类信息可能侵犯个人隐私，且在部分场景下易受行人外貌、衣着的干扰。与此同时，路由器的广泛普及为ReID提供了新的可能。本文提出一种利用WiFi信道状态信息（CSI）的方法，以WiFi信号的多径传播特性作为区分不同行人特征的依据。我们提出了一种可处理可变长度数据的双流网络结构，该结构分别在时域分析WiFi信号的幅度、在频域分析其相位，通过连续横向连接融合时频信息，并采用先进的目标函数进行表征与度量学习。在真实场景采集的数据集上测试，我们的方法取得了93.68%的mAP和98.13%的Rank-1。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
