用Wi-Fi信号统计图实现无需设备的高精度多人识别。
ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry
- 将短时信道状态信息转为统计图，用轻量Transformer提取特征。
- 6秒窗口准确率达78.88%，60秒聚合后达84.85%，Top-5超99%。
- 压缩注意力机制提升效率，推理计算量减少27倍，适合大规模部署。
被动式无设备人体识别是相机与可穿戴设备生物识别的替代方案，但现有无线方法多依赖步态或活动特征，且极少进行规模化评估。本文提出Argus，一种基于商用信道状态信息（CSI）的被动式Wi-Fi感知系统，无需附加设备或特定动作即可识别人体。Argus将短时CSI片段转换为紧凑的统计图（statgrams），通过轻量级解码器仅用的Transformer读取粗粒度统计图块作为令牌，并采用分段逻辑值聚合实现时间上的证据整合。在包含154名受试者的数据集上，采用严格物理分段划分，对6秒窗口的识别达到78.88%±1.62%的Top-1准确率，60秒分段聚合19个重叠窗口后提升至84.85%±1.31%；Top-3和Top-5准确率分别达98.61%和99.26%。相比原始CSI Transformer基线，Argus在每窗口仅使用4.4倍更少的浮点运算量的情况下，性能提升7.75个百分点。注意力引导的压缩机制在仅使用一半的EHealth图像块时仍保持单窗口全精度。在跨三间房、两频段的多用户基准测试集WiMANS上，Argus平均仅比最优配置基线低1.23个百分点，但推理浮点运算量减少27倍。结果表明，紧凑的CSI统计特征可支撑大规模被动识别，同时揭示了开集拒识与跨房间迁移中的实际限制。
原文摘要 · Abstract (English)
Passive, device-free person identification offers an alternative to camera- and wearable-based biometrics, yet existing wireless approaches rely largely on gait or activity cues and are rarely evaluated at scale. In this paper, we present \emph{Argus}, a passive Wi-Fi sensing system that identifies people from commodity Channel State Information (CSI) without requiring an attached device or a prescribed motion. Argus converts short CSI spans into compact \emph{statgrams}: statistical maps built from the channel views available on a given device. A lightweight decoder-only Transformer then reads coarse statgram patches as tokens, and segment-level logit aggregation combines evidence over time. On a 154-subject CSI dataset evaluated with a strict physical-segment split, Argus reaches $78.88\% \pm 1.62\%$ Top-1 accuracy on 6-second windows and $84.85\% \pm 1.31\%$ after aggregating 19 overlapping windows over a 60-second segment; Top-3 and Top-5 reach $98.61\%$ and $99.26\%$. For a 60-second statgram, Argus improves over a raw-CSI Transformer baseline by 7.75 points while using $4.4\times$ fewer FLOPs per window. Attention-guided compression preserves full single-window accuracy with only half of the EHealth patches. On WiMANS, a multi-user benchmark across three rooms and two Wi-Fi bands, Argus remains within 1.23 percentage points of the strongest per-configuration baselines on average while using $27\times$ fewer inference FLOPs. These results show that compact CSI statistics can scale passive identification while also exposing deployment limits in open-set rejection and cross-room transfer.
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