论文标题
基于WiFi的时空人类动作感知
WiFi-based Spatiotemporal Human Action Perception
论文作者
论文摘要
基于WiFi的人类活动识别（HAR）的感知最近已成为一个热门话题，因为与基于视频的HAR相比，它带来了巨大的好处，例如消除了视线（LOS）（LOS）和保留隐私的需求。但是，使WiFi信号“看到”动作非常粗糙，因此仍处于起步阶段。提出了端到端时空WiFi信号神经网络（STWNN），以在视线和非视线方案中启用仅WIFI的感应。特别是，3D卷积模块能够探索WiFi信号的时空连续性，并且功能自我发项模块可以显式保持主导特征。此外，WiFi信号的新型3D表示旨在保留多尺度的时空信息。此外，同步收集了一个小的无线视觉数据集（WVAR），以扩展Stwnn的潜力，从而通过遮挡延伸到“ See”。 WVAR和其他三个公共基准数据集的定量和定性结果证明了我们方法对准确性和转移一致性的有效性。
WiFi-based sensing for human activity recognition (HAR) has recently become a hot topic as it brings great benefits when compared with video-based HAR, such as eliminating the demands of line-of-sight (LOS) and preserving privacy. Making the WiFi signals to 'see' the action, however, is quite coarse and thus still in its infancy. An end-to-end spatiotemporal WiFi signal neural network (STWNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore the spatiotemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features. In addition, a novel 3D representation for WiFi signals is designed to preserve multi-scale spatiotemporal information. Furthermore, a small wireless-vision dataset (WVAR) is synchronously collected to extend the potential of STWNN to 'see' through occlusions. Quantitative and qualitative results on WVAR and the other three public benchmark datasets demonstrate the effectiveness of our approach on both accuracy and shift consistency.
