网络空间安全学院数据空间研究中心陈彦老师2023年6月3日-6月10日参加希腊ICASSP 2023会议，将在校内作相关分享报告。

报告时间：9月5号下午2：30；

报告地点：高新校区人才公寓1122会议室；

报告题目：Fast 3D Human Pose Estimation Using RF Signals。

欢迎感兴趣的各位师生参加。

                                                                         科研部

                                                                         2023年9月1日

报告人简介：

陈彦，图书馆VIP教授、博导，国家创新人才青年项目入选者。发表专著两部（Cambridge University Press、Springer）、学术论文200多篇，其中IEEE期刊论文100多篇。研究成果被IEEE Spectrum、Communications of the ACM、央广网、光明网、中新网、Daily News Era、The Star、Asiaone等几十个国内外主流媒体报道。获IEEE GLOBECOM 2013和APSIPA ASC 2020最佳论文奖、IEEE ICASSP 2016和PCM 2017最佳学生论文奖、IEEE MMSP2022最佳论文奖 Runner-Up等。担任国际期刊IEEE TNSE和IEEE TSIPN副编辑，担任APSIPA SIPTM专委会主任、APSIPA Distinguished Lecturer (2020-2021)、IEEE传感器阵列与多通道专委会委员等，担任APSIPA ASC 2022、2021程序委员会联合主席、ACM MM2021领域主席、IEEE WCSP2019多媒体通信专题主席等。

Abstract:

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.