用Wi-Fi信号识别行人，突破光照和遮挡限制
WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding
- 通过无线信道状态信息提取人体生物特征
- 在NTU-Fi数据集上达到领先水平的识别准确率
- 适合隐私敏感场景下的无摄像头身份验证
行人重识别是视频监控中的关键挑战任务。传统方法依赖视觉数据，但光照不足、遮挡和拍摄角度不佳常导致性能下降。为此，我们提出WhoFi，一种利用Wi-Fi信号进行行人重识别的新方法。从信道状态信息（CSI）中提取生物特征，并通过基于Transformer的模块化深度神经网络进行处理。采用批内负样本损失函数训练网络，以学习鲁棒且通用的生物特征表示。在NTU-Fi数据集上的实验表明，该方法性能优于或媲美现有最优方法，验证了其通过Wi-Fi信号识别个体的有效性。
原文摘要 · Abstract (English)
Person Re-Identification is a key and challenging task in video surveillance. While traditional methods rely on visual data, issues like poor lighting, occlusion, and suboptimal angles often hinder performance. To address these challenges, we introduce WhoFi, a novel pipeline that utilizes Wi-Fi signals for person re-identification. Biometric features are extracted from Channel State Information (CSI) and processed through a modular Deep Neural Network (DNN) featuring a Transformer-based encoder. The network is trained using an in-batch negative loss function to learn robust and generalizable biometric signatures. Experiments on the NTU-Fi dataset show that our approach achieves competitive results compared to state-of-the-art methods, confirming its effectiveness in identifying individuals via Wi-Fi signals.
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