부산대학교 도서관

Existing deep learning-based wireless sensing models usually require intensive computation. In this paper, we introduce a lightweight RF-based 3D human pose estimation model, i.e., Fast RFPose, to enable real-time human pose estimation. Specifically, Fast RFPose first estimates the human locations in the RF heatmap and crops the human location regions, then estimates the fine-grained human poses based on the cropped small RF heatmaps. In the experiments, we build a radio system and a multi-view camera system to acquire the RF signals and the ground-truth human poses, and compare Fast RFPose with state-of-the-art methods. Experimental results demonstrate that Fast RFPose outperforms the alternative methods. Besides, we further deploy the trained Fast RFPose model on a laptop with a CPU and Fast RFPose can achieve 66 FPS processing speed, which means it can meet the real-time running requirements in mobile devices.