基于4D成像雷达的隔墙人体姿态重建与行为识别研究

张锐; 龚汉钦; 宋瑞源; 李亚东; 卢智; 张东恒; 胡洋; 陈彦

doi:10.12000/JR24132

基于4D成像雷达的隔墙人体姿态重建与行为识别研究

doi: 10.12000/JR24132

中国科学技术大学网络空间安全学院合肥 230026

基金项目: 国家自然科学基金(62172381, 62201542)

详细信息

作者简介:
张　锐，博士生，主要研究方向为人体感知、视频图像去噪

龚汉钦，硕士生，主要研究方向为无线感知

宋瑞源，博士生，主要研究方向为多模态机器学习

李亚东，硕士生，主要研究方向为毫米波雷达成像

卢　智，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

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

通讯作者:
陈彦 eecyan@ustc.edu.cn

责任主编：丁一鹏 Corresponding Editor: DING Yipeng
中图分类号: TN957.51
计量
- 文章访问数: 184
- HTML全文浏览量: 41
- PDF下载量: 41
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-01
- 修回日期: 2024-08-11
- 网络出版日期: 2024-09-03

Through-wall Human Pose Reconstruction and Action Recognition Using Four-dimensional Imaging Radar

School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China

Funds: The National Natural Science Foundation of China (62172381, 62201542)

More Information

Corresponding author: CHEN Yan, eecyan@ustc.edu.cn

摘要

摘要: 隔墙人体姿态重建和行为识别在智能安防和虚拟现实等领域具有广泛应用前景。然而，现有隔墙人体感知方法通常忽视了对4D时空特征的建模以及墙体对信号的影响，针对这些问题，该文创新性地提出了一种基于4D成像雷达的隔墙人体感知新架构。首先，基于时空分离的分步策略，该文设计了ST²W-AP时空融合网络，解决了由于主流深度学习库缺少4D卷积而无法充分利用多帧3D体素时空域信息的问题，实现了保留3D空域信息的同时利用长序时域信息，大幅提升姿态估计任务和行为识别任务的性能。此外，为抑制墙体对信号的干扰，该文利用深度学习强大的拟合性能和并行输出的特点设计了深度回波域补偿器，降低了传统墙体补偿方法的计算开销。大量的实验结果表明，相比于现有最佳方法，ST²W-AP将平均关节位置误差降低了33.57%，并且将行为识别的F1分数提高了0.51%。
- 穿墙 /
- 人体姿态估计 /
- 行为识别 /
- 射频感知 /
- 深度学习
Abstract: Through-wall human pose reconstruction and behavior recognition have enormous potential in fields like intelligent security and virtual reality. However, existing methods for through-wall human sensing often fail to adequately model four-Dimensional (4D) spatiotemporal features and overlook the influence of walls on signal quality. To address these issues, this study proposes an innovative architecture for through-wall human sensing using a 4D imaging radar. The core of this approach is the ST²W-AP fusion network, which is designed using a stepwise spatiotemporal separation strategy. This network overcomes the limitations of mainstream deep learning libraries that currently lack 4D convolution capabilities, which hinders the effective use of multiframe three-Dimensional (3D) voxel spatiotemporal domain information. By preserving 3D spatial information and using long-sequence temporal information, the proposed ST²W-AP network considerably enhances the pose estimation and behavior recognition performance. Additionally, to address the influence of walls on signal quality, this paper introduces a deep echo domain compensator that leverages the powerful fitting performance and parallel output characteristics of deep learning, thereby reducing the computational overhead of traditional wall compensation methods. Extensive experimental results demonstrate that compared with the best existing methods, the ST²W-AP network reduces the average joint position error by 33.57% and improves the F1 score for behavior recognition by 0.51%.
- Through-wall /
- Human pose estimation /
- Activity recognition /
- RF sensing /
- Deep learning

HTML全文

图 1 穿墙姿态估计和行为识别框架流程

Figure 1. Pipeline of the proposed through-wall pose estimation and activity recognition framework

下载: 全尺寸图片幻灯片

图 2 实验场景和无线信号传播路径

Figure 2. Experiment scenario and RF propagation path

下载: 全尺寸图片幻灯片

图 3 墙体补偿器结构图

Figure 3. Structural diagram of wall compensator

下载: 全尺寸图片幻灯片

图 4 不带墙体补偿和带墙体补偿的隔墙成像

Figure 4. BP imaging w/o and w/compensating

下载: 全尺寸图片幻灯片

图 5 网络结构图及P4D模块的3种设计(B为批大小，T为时域序列长度，C为输入通道数，H为高度，W为宽度，以及D为深度。C1,C2为卷积输入通道，S为步长)

Figure 5. Network structure diagram and three designs of the pseudo P4D module (B represents the batch size, T denotes the length of the temporal sequence, C indicates the number of input channels, H stands for height, W denotes width, and D represents depth. C1 and C2 represent the input channels for the convolution, while S denotes the stride)

下载: 全尺寸图片幻灯片

图 6 动态权重变化曲线

Figure 6. Dynamic weight variation curve

下载: 全尺寸图片幻灯片

图 7 不同场景姿态重建可视化

Figure 7. Visualization of pose reconstruction across different scenarios

下载: 全尺寸图片幻灯片

图 8 时间序列长度为12帧的4D成像结果

Figure 8. 4D imaging results with a time series length of 12 frames

下载: 全尺寸图片幻灯片

图 9 不同方法在行为识别任务的混淆矩阵

Figure 9. Confusion matrices for activity recognition task across different methods

下载: 全尺寸图片幻灯片

图 10 特征t-SNE可视化

Figure 10. t-SNE visualization of the learned feature

下载: 全尺寸图片幻灯片

图 11 天线单元选取路径

Figure 11. Selection Path of Antenna Units

下载: 全尺寸图片幻灯片

表 1 雷达系统参数

Table 1. Key parameters of radar system

参数	数值
中心频率	1.8 GHz
带宽	2.0 GHz
频点	201
帧率	12帧/s
发射天线阵列长度	59.4 cm
接收天线阵列长度	59.4 cm

下载: 导出CSV

表 2 人体关节点重建误差定量评估结果(mm)

Table 2. Quantitative evaluation results of human body joint reconstruction error (mm)

方法	鼻子	脖子	肩膀	手肘	手腕	臀部	膝盖	脚踝	眼睛	耳朵	平均值
RF-Pose3D^[22]	98.57	51.52	73.07	88.70	98.32	76.76	65.32	91.08	85.06	71.67	80.55
ResNet3D-50^[39]	82.42	65.55	71.58	86.61	118.98	64.83	68.72	79.26	84.41	74.02	80.27
Dual-task Net^[27]	136.09	104.37	109.49	129.81	175.06	100.37	98.50	113.55	126.68	117.09	121.20
RadarFormer^[28]	367.52	318.65	328.37	360.11	418.66	313.13	302.99	294.66	359.07	338.58	339.85
ST²W-AP	56.39	44.93	47.26	55.41	71.16	44.74	47.68	53.79	58.86	50.34	53.32

下载: 导出CSV

表 3 不同场景关节点重建误差定量评估结果(mm)

Table 3. Quantitative evaluation results of joint reconstruction error in different scenarios (mm)

方法	行走	坐下	挥手	静止姿态
RF-Pose3D^[22]	96.74	76.28	58.77	51.08
ResNet3D-50^[39]	104.63	61.25	57.26	44.29
Dual-task Net^[27]	156.31	75.15	100.80	100.85
RadarFormer^[28]	545.00	194.29	98.12	216.34
ST²W-AP	69.25	40.38	37.30	37.81

下载: 导出CSV

表 4 行为识别定量评估结果

Table 4. Quantitative evaluation results of behavior recognition

方法	准确率	召回率	精确率	F1分数
ResNet3D-50^[39]	0.9967	0.9969	0.9930	0.9949
Dual-task Net^[27]	0.9984	0.9741	0.9823	0.9727
RadarFormer^[28]	0.9519	0.5066	0.4513	0.4502
ST²W-AP	1.0000	1.0000	1.0000	1.0000

下载: 导出CSV

表 5 选择路径1下的补偿器RMSPE相对误差分布

Table 5. Distribution of compensator accuracy under selection path 1

折次	12	24	30	40	60	120	无补偿
第1折	0.00427	0.00476	0.00422	0.00399	0.00302	0.00235	0.01399
第2折	0.00405	0.00385	0.00238	0.00211	0.00158	0.00127	0.01382
第3折	0.00373	0.00362	0.00210	0.00194	0.00225	0.00124	0.01363
第4折	0.00332	0.00290	0.00183	0.00181	0.00218	0.00125	0.01335
第5折	0.00370	0.00332	0.00224	0.00180	0.00161	0.00151	0.01315
第6折	0.00473	0.00398	0.00342	0.00344	0.00258	0.00225	0.01294
平均	0.00397	0.00374	0.00270	0.00252	0.00220	0.00165	0.01348

下载: 导出CSV

表 6 选择路径2下的补偿器RMSPE相对误差分布

Table 6. Distribution of compensator accuracy under selection path 2

折次	12	24	30	40	60	120	无补偿
第1折	0.00455	0.00492	0.00273	0.00315	0.00267	0.00201	0.01413
第2折	0.00386	0.00492	0.00241	0.00225	0.00260	0.00149	0.01378
第3折	0.00370	0.00305	0.00202	0.00283	0.00155	0.00130	0.01352
第4折	0.00336	0.00347	0.00192	0.00209	0.00236	0.00137	0.01348
第5折	0.00365	0.00333	0.00336	0.00349	0.00273	0.00188	0.01312
第6折	0.00479	0.00507	0.00292	0.00312	0.00293	0.00215	0.01284
平均	0.00399	0.00413	0.00256	0.00282	0.00247	0.00170	0.01348

下载: 导出CSV

表 7 计算时间开销对比

Table 7. Comparison of time overheads

墙体补偿方法	推理时间(s)
传统补偿方法^[29]	15914.359
深度回波域补偿器	9.066

下载: 导出CSV

表 8 自由空间行为识别和姿态重建定量评估结果

Table 8. Quantitative evaluation results of free-space behavior recognition and pose estimation

指标	数值
准确率召回率精确率 F1分数 MPJPE (mm)	0.9990 0.9997 0.9831 0.9905 42.68

下载: 导出CSV

表 9 行为识别和姿态重建定量评估结果

Table 9. Quantitative evaluation results of behavior recognition and pose estimation

方法	准确率	召回率	精确率	F1分数	MPJPE (mm)
ST²W-AP-PA	1.0000	1.0000	1.0000	1.0000	53.32
ST²W-AP-PB	1.0000	1.0000	1.0000	1.0000	54.36
ST²W-AP-PC	1.0000	1.0000	1.0000	1.0000	55.49
ST²W-AP18	0.9973	0.9994	0.9917	0.9952	155.30
ST²W-AP101	1.0000	1.0000	1.0000	1.0000	53.29
ST²W-AP-50 w/o com	0.8952	0.7863	0.9004	0.8370	192.14
ST²W-AP-50 w/o dw	0.9998	1.0000	1.0000	1.0000	54.46
注：w/o com 表示去除墙体补偿，w/o dw表示去除动态权重调整策略。

下载: 导出CSV

参考文献(43)

[1]	杨小鹏, 高炜程, 渠晓东. 基于微多普勒角点特征与Non-Local机制的穿墙雷达人体步态异常终止行为辨识技术[J]. 雷达学报, 2024, 13(1): 68–86. doi: 10.12000/JR23181. YANG Xiaopeng, GAO Weicheng, and QU Xiaodong. Human anomalous gait termination recognition via through-the-wall radar based on micro-Doppler corner features and Non-Local mechanism[J]. Journal of Radars, 2024, 13(1): 68–86. doi: 10.12000/JR23181.
[2]	金添, 宋勇平, 崔国龙, 等. 低频电磁波建筑物内部结构透视技术研究进展[J]. 雷达学报, 2020, 10(3): 342–359. doi: 10.12000/JR20119. JIN Tian, SONG Yongping, CUI Guolong, et al. Advances on penetrating imaging of building layout technique using low frequency radio waves[J]. Journal of Radars, 2021, 10(3): 342–359. doi: 10.12000/JR20119.
[3]	崔国龙, 余显祥, 魏文强, 等. 认知智能雷达抗干扰技术综述与展望[J]. 雷达学报, 2022, 11(6): 974–1002. doi: 10.12000/JR22191. CUI Guolong, YU Xianxiang, WEI Wenqiang, et al. An overview of antijamming methods and future works on cognitive intelligent radar[J]. Journal of Radars, 2022, 11(6): 974–1002. doi: 10.12000/JR22191.
[4]	夏正欢, 张群英, 叶盛波, 等. 一种便携式伪随机编码超宽带人体感知雷达设计[J]. 雷达学报, 2015, 4(5): 527–537. doi: 10.12000/JR15027. XIA Zhenghuan, ZHANG Qunying, YE Shengbo, et al. Design of a handheld pseudo random coded UWB radar for human sensing[J]. Journal of Radars, 2015, 4(5): 527–537. doi: 10.12000/JR15027.
[5]	金添, 宋勇平. 超宽带雷达建筑物结构稀疏成像[J]. 雷达学报, 2018, 7(3): 275–284. doi: 10.12000/JR18031. JIN Tian and SONG Yongping. Sparse imaging of building layouts in ultra-wideband radar[J]. Journal of Radars, 2018, 7(3): 275–284. doi: 10.12000/JR18031.
[6]	丁一鹏, 厍彦龙. 穿墙雷达人体动作识别技术的研究现状与展望[J]. 电子与信息学报, 2022, 44(4): 1156–1175. doi: 10.11999/JEIT211051. DING Yipeng and SHE Yanlong. Research status and prospect of human movement recognition technique using through-wall radar[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1156–1175. doi: 10.11999/JEIT211051.
[7]	刘天亮, 谯庆伟, 万俊伟, 等. 融合空间-时间双网络流和视觉注意的人体行为识别[J]. 电子与信息学报, 2018, 40(10): 2395–2401. doi: 10.11999/JEIT171116. LIU Tianliang, QIAO Qingwei, WAN Junwei, et al. Human action recognition via spatio-temporal dual network flow and visual attention fusion[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2395–2401. doi: 10.11999/JEIT171116.
[8]	MOHAN R and VALADA A. EfficientPS: Efficient panoptic segmentation[J]. International Journal of Computer Vision, 2021, 129(5): 1551–1579. doi: 10.1007/s11263-021-01445-z.
[9]	LAZAROW J, LEE K, SHI Kunyu, et al. Learning instance occlusion for panoptic segmentation[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 10717–10726. doi: 10.1109/CVPR42600.2020.01073.
[10]	ZHANG Dongheng, HU Yang, and CHEN Yan. MTrack: Tracking multiperson moving trajectories and vital signs with radio signals[J]. IEEE Internet of Things Journal, 2021, 8(5): 3904–3914. doi: 10.1109/JIOT.2020.3025820.
[11]	LI Yadong, ZHANG Dongheng, CHEN Jinbo, et al. Towards domain-independent and real-time gesture recognition using mmWave signal[J]. IEEE Transactions on Mobile Computing, 2023, 22(12): 7355–7369. doi: 10.1109/TMC.2022.3207570.
[12]	ZHANG Binbin, ZHANG Dongheng, LI Yadong, et al. Unsupervised domain adaptation for RF-based gesture recognition[J]. IEEE Internet of Things Journal, 2023, 10(23): 21026–21038. doi: 10.1109/JIOT.2023.3284496.
[13]	SONG Ruiyuan, ZHANG Dongheng, WU Zhi, et al. RF-URL: Unsupervised representation learning for RF sensing[C]. The 28th Annual International Conference on Mobile Computing and Networking, Sydney, Australia, 2022: 282–295. doi: 10.1145/3495243.3560529.
[14]	GONG Hanqin, ZHANG Dongheng, CHEN Jinbo, et al. Enabling orientation-free mmwave-based vital sign sensing with multi-domain signal analysis[C]. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024: 8751–8755. doi: 10.1109/ICASSP48485.2024.10448323.
[15]	XIE Chunyang, ZHANG Dongheng, WU Zhi, et al. RPM 2.0: RF-based pose machines for multi-person 3D pose estimation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(1): 490–503. doi: 10.1109/TCSVT.2023.3287329.
[16]	YANG Shuai, ZHANG Dongheng, SONG Ruiyuan, et al. Multiple WiFi access points co-localization through joint AoA estimation[J]. IEEE Transactions on Mobile Computing, 2024, 23(2): 1488–1502. doi: 10.1109/TMC.2023.3239377.
[17]	WU Zhi, ZHANG Dongheng, XIE Chunyang, et al. RFMask: A simple baseline for human silhouette segmentation with radio signals[J]. IEEE Transactions on Multimedia, 2023, 25: 4730–4741. doi: 10.1109/TMM.2022.3181455.
[18]	GENG Ruixu, HU Yang, LU Zhi, et al. Passive non-line-of-sight imaging using optimal transport[J]. IEEE Transactions on Image Processing, 2022, 31: 110–124. doi: 10.1109/TIP.2021.3128312.
[19]	XIE Chunyang, ZHANG Dongheng, WU Zhi, et al. RPM: RF-based pose machines[J]. IEEE Transactions on Multimedia, 2024, 26: 637–649. doi: 10.1109/TMM.2023.3268376.
[20]	YU Cong, ZHANG Dongheng, WU Zhi, et al. MobiRFPose: Portable RF-based 3D human pose camera[J]. IEEE Transactions on Multimedia, 2024, 26: 3715–3727. doi: 10.1109/TMM.2023.3314979.
[21]	ZHAO Mingmin, LI Tianhong, ABU ALSHEIKH M, et al. Through-wall human pose estimation using radio signals[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7356–7365. doi: 10.1109/CVPR.2018.00768.
[22]	ZHAO Mingmin, TIAN Yonglong, ZHAO Hang, et al. RF-based 3D skeletons[C]. The 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary, 2018: 267–281. doi: 10.1145/3230543.3230579.
[23]	YU Cong, ZHANG Dongheng, WU Zhi, et al. Fast 3D human pose estimation using RF signals[C]. ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023: 1–5. doi: 10.1109/ICASSP49357.2023.10094778.
[24]	JIANG Wenjun, XUE Hongfei, MIAO Chenglin, et al. Towards 3D human pose construction using wifi[C]. The 26th Annual International Conference on Mobile Computing and Networking, London, UK, 2020: 23. doi: 10.1145/3372224.3380900.
[25]	ZHENG Zhijie, PAN Jun, NI Zhikang, et al. Recovering human pose and shape from through-the-wall radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5112015. doi: 10.1109/TGRS.2022.3162333.
[26]	ZHENG Zhijie, PAN Jun, ZHANG Diankun, et al. Through-wall human pose estimation by mutual information maximizing deeply supervised nets[J]. IEEE Internet of Things Journal, 2024, 11(2): 3190–3205. doi: 10.1109/JIOT.2023.3294955.
[27]	SONG Yongkun, DAI Yongpeng, JIN Tian, et al. Dual-task human activity sensing for pose reconstruction and action recognition using 4-D imaging radar[J]. IEEE Sensors Journal, 2023, 23(19): 23927–23940. doi: 10.1109/JSEN.2023.3308788.
[28]	ZHENG Zhijie, ZHANG Diankun, LIANG Xiao, et al. RadarFormer: End-to-end human perception with through-wall radar and transformers[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(10): 4319–4332. doi: 10.1109/TNNLS.2023.3314031.
[29]	AHMAD F, AMIN M G, and KASSAM S A. Synthetic aperture beamformer for imaging through a dielectric wall[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(1): 271–283. doi: 10.1109/TAES.2005.1413761.
[30]	ZHANG Zhengyou. A flexible new technique for camera calibration[J]. IEEE Transactions on pattern analysis and machine intelligence, 2000, 22(11): 1330–1334. doi: 10.1109/34.888718.
[31]	FANG Haoshu, LI Jiefeng, TANG Hongyang, et al. AlphaPose: Whole-body regional multi-person pose estimation and tracking in real-time[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(6): 7157–7173. doi: 10.1109/TPAMI.2022.3222784.
[32]	HE Ying, ZHANG Dongheng, and CHEN Yan. 3D radio imaging under low-rank constraint[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(8): 3833–3847. doi: 10.1109/TCSVT.2023.3239683.
[33]	刘剑刚. 穿墙雷达隐蔽目标成像跟踪方法研究[D]. [博士学位论文], 电子科技大学, 2017. LIU Jiangang. Imaging-tracking technology for hidden targets of a through-the-wall radar[D]. [Ph.D. dissertation], University of Electronic Science and Technology of China, 2017.
[34]	AHMAD F, ZHANG Yimin, and AMIN M G. Three-dimensional wideband beamforming for imaging through a single wall[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(2): 176–179. doi: 10.1109/LGRS.2008.915742.
[35]	CYBENKO G. Approximation by superpositions of a sigmoidal function[J]. Mathematics of Control, Signals and Systems, 1989, 2(4): 303–314. doi: 10.1007/BF02551274.
[36]	KARNIADAKIS G E, KEVREKIDIS I G, LU Lu, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422–440. doi: 10.1038/s42254-021-00314-5.
[37]	ADIB F, HSU C Y, MAO Hongzi, et al. Capturing the human figure through a wall[J]. ACM Transactions on Graphics (TOG), 2015, 34(6): 219. doi: 10.1145/2816795.2818072.
[38]	QIU Zhaofan, YAO Ting, and MEI Tao. Learning spatio-temporal representation with pseudo-3D residual networks[C]. The IEEE International Conference on Computer Vision, Venice, Italy, 2017: 5534–5542. doi: 10.1109/ICCV.2017.590.
[39]	HARA K, KATAOKA H, and SATOH Y. Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet?[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6546–6555. doi: 10.1109/CVPR.2018.00685.
[40]	KATAOKA H, HARA K, HAYASHI R, et al. Spatiotemporal initialization for 3D CNNs with generated motion patterns[C]. The IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2022: 737–746. doi: 10.1109/WACV51458.2022.00081.
[41]	JI Shuiwang, XU Wei, YANG Ming, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1): 221–231. doi: 10.1109/TPAMI.2012.59.
[42]	IONESCU C, PAPAVA D, OLARU V, et al. Human3. 6M: Large scale datasets and predictive methods for 3D human sensing in natural environments[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 36(7): 1325–1339. doi: 10.1109/TPAMI.2013.248.
[43]	VAN DER MAATEN L and HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(86): 2579–2605.