Abstract
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.
Abstract (translated)
人员重新识别是视频监控中的一个关键且具有挑战性的任务。传统方法依赖于视觉数据，但由于照明不良、遮挡和视角不佳等问题，其性能常常受到限制。为了解决这些挑战，我们提出了一种名为WhoFi的新颖管道系统，该系统利用Wi-Fi信号来进行人员重新识别。从信道状态信息（CSI）中提取生物特征，并通过一个模块化的深度神经网络进行处理，该网络采用基于Transformer的编码器。通过批内负样本损失函数对该网络进行训练，以学习稳健且通用性强的生物签名。 在NTU-Fi数据集上的实验表明，我们提出的方法与当前最先进的方法相比取得了具有竞争力的结果，这证实了利用Wi-Fi信号识别个体的有效性。
URL
https://arxiv.org/abs/2507.12869
