基于无线信号的人体姿态估计综述

陈彦 张锐 李亚东 宋瑞源 耿瑞旭 龚汉钦 汪斌全 张东恒 胡洋

陈彦, 张锐, 李亚东, 等. 基于无线信号的人体姿态估计综述[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24189
引用本文: 陈彦, 张锐, 李亚东, 等. 基于无线信号的人体姿态估计综述[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24189
CHEN Yan, ZHANG Rui, LI Yadong, et al. An overview of human pose estimation based on wireless signals[J]. Journal of Radars, in press. doi: 10.12000/JR24189
Citation: CHEN Yan, ZHANG Rui, LI Yadong, et al. An overview of human pose estimation based on wireless signals[J]. Journal of Radars, in press. doi: 10.12000/JR24189

基于无线信号的人体姿态估计综述

DOI: 10.12000/JR24189
基金项目: 国家自然科学基金(62172381, 62201542)
详细信息
    作者简介:

    陈 彦,博士,教授,主要研究方向为多模态感知、多媒体信号处理和数字健康

    张 锐,博士生,主要研究方向为多模态感知、视频图像去噪

    李亚东,博士生,主要研究方向为毫米波雷达成像

    宋瑞源,博士生,主要研究方向为多模态机器学习

    耿瑞旭,博士生,主要研究方向为毫米波雷达成像

    龚汉钦,博士生,主要研究方向为无线感知

    汪斌全,博士后,主要研究方向为无线感知

    张东恒,博士,副研究员,主要研究方向为无线感知

    胡 洋,博士,副教授,主要研究方向为计算机视觉、多媒体信号处理和多模态感知

    通讯作者:

    陈彦 eecyan@ustc.edu.cn

  • 责任主编:金添 Corresponding Editor: JIN Tian
  • 中图分类号: TN957.51

An Overview of Human Pose Estimation Based on Wireless Signals

Funds: The National Natural Science Foundation of China (62172381, 62201542)
More Information
  • 摘要: 人体姿态估计在人机交互、动作捕捉和虚拟现实等领域具有广泛的应用前景,一直是人体感知研究的重要方向。然而,基于光学图像的姿态估计方法往往受限于光照条件和隐私问题。因此,利用可在各种光照遮挡下工作,且具有隐私保护性的无线信号进行人体姿态估计获得了更多关注。根据无线信号的工作频率,现有技术可分为高频方法和低频方法,且不同的信号频率对应硬件系统、信号特性、噪声处理和深度学习算法设计等方面均有所不同。该文将以毫米波雷达、穿墙雷达和WiFi信号为代表,回顾其在人体姿态重建研究中的进展和代表性工作,分析各类信号模式的优势与局限,并对潜在研究难点以及未来发展趋势进行了展望。

     

  • 图  1  人体姿态模型

    Figure  1.  Human pose models

    图  2  RPM模型框架图[18]

    Figure  2.  Diagram of the RPM framework[18]

    图  3  基于成像的人体姿态估计方法

    Figure  3.  Radar imaging-based human pose estimation methods

    图  4  混凝土墙体对于信号传播路径的影响

    Figure  4.  The impact of concrete walls on signal propagation paths

    图  5  Person-in-WiFi 3D模型框架[42]

    Figure  5.  The framework of Person-in-WiFi 3D[42]

    表  1  基于无线信号的人体姿态估计研究现状总结

    Table  1.   Summary of research status on pose estimation based on wireless signals

    基于频率的分类 设备 雷达特征信息 代表性工作
    基于高频无线信号的
    人体姿态估计
    毫米波雷达
    (30~300 GHz)
    3D point cloud mmPose[29]
    Heatmap RPM[18]
    Heatmap RPM 2.0[14]
    Heatmap MobiRFPose[19]
    基于低频无线信号的
    人体姿态估计
    穿墙雷达
    (300 MHz~10 GHz)
    Heatmap RF-Pose[34]
    Heatmap RF-Pose3D[36]
    单帧3D成像体素 MIMDSN[37]
    多帧3D成像体素 ST2W-AP[38]
    Heatmap和3D成像体素 Dual-task Net[39]
    多帧雷达回波 RadarFormer[40]
    WiFi
    (2.400~5.825 GHz)
    Channel state information Person-in-WiFi[41]
    Channel state information Person-in-WiFi 3D[42]
    Channel state information DensePose From WiFi[43]
    下载: 导出CSV

    表  2  基于无线信号的人体姿态估计数据集对比

    Table  2.   Summary of dataset on pose estimation based on wireless signals

    数据集 无线设备 真值采集设备 场景数量 行为种类 用户数量 总样本数(帧)
    UWB-HA4D-1.0 穿墙雷达 RGB 3 10 11 110280
    HIBER 毫米波雷达 RGB 10 4 10 402380
    RT-Pose 毫米波雷达 RGB
    LiDAR
    40 6 10 72000
    mRI 毫米波雷达 RGB-D
    IMU
    1 12 20 160000
    mmBody 毫米波雷达 RGB 100 7 20 >20万
    HuPR 毫米波雷达 RGB 1 3 6 141000
    下载: 导出CSV
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  • 收稿日期:  2024-09-16
  • 修回日期:  2024-11-07
  • 网络出版日期:  2024-11-26

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