Zhou Yue, Xu Caojie, Zhao Lu, Zhu Aichun, Hu Fangqiang, Li Yifeng
School of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China.
Entropy (Basel). 2022 Dec 22;25(1):20. doi: 10.3390/e25010020.
Cross-modal human pose estimation has a wide range of applications. Traditional image-based pose estimation will not work well in poor light or darkness. Therefore, some sensors such as LiDAR or Radio Frequency (RF) signals are now using to estimate human pose. However, it limits the application that these methods require much high-priced professional equipment. To address these challenges, we propose a new WiFi-based pose estimation method. Based on the Channel State Information (CSI) of WiFi, a novel architecture CSI-former is proposed to innovatively realize the integration of the multi-head attention in the WiFi-based pose estimation network. To evaluate the performance of CSI-former, we establish a span-new dataset Wi-Pose. This dataset consists of 5 GHz WiFi CSI, the corresponding images, and skeleton point annotations. The experimental results on Wi-Pose demonstrate that CSI-former can significantly improve the performance in wireless pose estimation and achieve more remarkable performance over traditional image-based pose estimation. To better benefit future research on the WiFi-based pose estimation, Wi-Pose has been made publicly available.
跨模态人体姿态估计有广泛的应用。传统的基于图像的姿态估计在光线不好或黑暗环境中效果不佳。因此，现在一些传感器如激光雷达或射频（RF）信号被用于估计人体姿态。然而，这些方法需要昂贵的专业设备，这限制了其应用。为应对这些挑战，我们提出一种新的基于WiFi的姿态估计方法。基于WiFi的信道状态信息（CSI），提出了一种新颖的架构CSI-former，以创新性地在基于WiFi的姿态估计网络中实现多头注意力的集成。为评估CSI-former的性能，我们建立了一个全新的数据集Wi-Pose。该数据集由5GHz WiFi CSI、相应图像和骨架点注释组成。在Wi-Pose上的实验结果表明，CSI-former能显著提高无线姿态估计的性能，并且比传统的基于图像的姿态估计表现更出色。为了更好地推动未来基于WiFi的姿态估计研究，Wi-Pose已公开可用。
