基于点云时空特征的超宽带雷达轻量化人体行为识别方法

宋永坤; 晏天兴; 张可; 刘显; 戴永鹏; 金添

doi:10.12000/JR24110

基于点云时空特征的超宽带雷达轻量化人体行为识别方法

doi: 10.12000/JR24110

宋永坤^1, ,,
晏天兴¹,
张可¹,
刘显¹,
戴永鹏²,
金添²

1.
长沙理工大学计算机与通信工程学院长沙 410114
2.
国防科技大学电子科学学院长沙 410073

基金项目: 湖南省自然科学基金青年基金项目(2024JJ6065)

详细信息

作者简介:
宋永坤，博士，讲师，主要研究方向为雷达图像处理、新体制雷达技术、人体行为智能感知

晏天兴，硕士生，主要研究方向为动作识别、雷达信号处理

张　可，硕士生，主要研究方向为深度学习、姿态重构

刘　显，硕士生，主要研究方向为深度学习、目标检测

戴永鹏，博士，讲师，主要研究方向为MIMO阵列雷达成像与图像增强

金　添，博士，教授，主要研究方向为新体制雷达系统、智能感知与处理

通讯作者:
宋永坤 songyk1118@163.com

责任主编：郭世盛 Corresponding Editor: GUO Shisheng
中图分类号: TN957
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出版历程
- 收稿日期: 2024-06-05
- 修回日期: 2024-07-24
- 网络出版日期: 2024-08-28

A Lightweight Human Activity Recognition Method for Ultra-wideband Radar Based on Spatiotemporal Features of Point Clouds

1.
College of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China
2.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: The Youth Fund Project of the Hunan Provincial Natural Science Foundation (2024JJ6065)

More Information

Corresponding author: SONG Yongkun, songyk1118@163.com

摘要

摘要: 低频超宽带(UWB)雷达因其良好穿透性和分辨率，在人体行为识别领域具有显著的优势。针对现有的动作识别算法运算量大、网络参数多的问题，该文提出了一种基于时空点云的高效且轻量的超宽带雷达人体行为识别方法。首先通过UWB雷达采集人体的四维运动数据，然后采用离散采样的方法将雷达图像转换为点云表示，由于人体行为识别属于时间序列上的分类问题，该文结合PointNet++网络与Transformer网络提出了一种轻量化的时空网络，通过提取并分析四维点云的时空特征，实现了对人体行为的端到端识别。在模型的训练过程中，提出了一种点云数据多阈值融合的方法，进一步提高了模型的泛化性和识别能力。该文根据公开的四维雷达成像数据集对所提方法进行验证，并与现有方法进行了比较。结果表明，所提方法在人体行为识别率达到96.75%，且消耗较少的参数量和运算量，验证了其有效性。
- 超宽带雷达 /
- 行为识别 /
- 点云 /
- PointNet++ /
- Transformer
Abstract: Low-frequency Ultra-WideBand (UWB) radar offers significant advantages in the field of human activity recognition owing to its excellent penetration and resolution. To address the issues of high computational complexity and extensive network parameters in existing action recognition algorithms, this study proposes an efficient and lightweight human activity recognition method using UWB radar based on spatiotemporal point clouds. First, four-dimensional motion data of the human body are collected using UWB radar. A discrete sampling method is then employed to convert the radar images into point cloud representations. Because human activity recognition is a classification problem on time series, this paper combines the PointNet++ network with the Transformer network to propose a lightweight spatiotemporal network. By extracting and analyzing the spatiotemporal features of four-dimensional point clouds, end-to-end human activity recognition is achieved. During the model training process, a multithreshold fusion method is proposed for point cloud data to further enhance the model’s generalization and recognition capabilities. The proposed method is then validated using a public four-dimensional radar imaging dataset and compared with existing methods. The results show that the proposed method achieves a human activity recognition rate of 96.75% while consuming fewer parameters and computational resources, thereby verifying its effectiveness.
- Ultra-Wideband Badar (UWB) /
- Action recognition /
- Point clouds /
- PointNet++ /
- Transformer

HTML全文

图 1 步进频连续波形频域图

Figure 1. Frequency-domain diagram of SFCW waveform

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图 2 MIMO雷达工作场景

Figure 2. Operating scenarios of MIMO radar systems

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图 3 张开双臂动作“体”的数据

Figure 3. Data of the ‘body’ in the action of spreading arms

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图 4 不同动作经雷达回波转换后的三维等值面图

Figure 4. Three-dimensional isosurface diagrams of different actions converted from radar echo data

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图 5 不同点云数目所构建的人体模型

Figure 5. Human body models constructed from point clouds of varying quantities

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图 6 UWB-PointTransformer网络结构图

Figure 6. Schematic diagram of the UWB-PointTransformer network architecture

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图 7 Transformer网络

Figure 7. Transformer network

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图 8 数据采集使用的MIMO超宽带雷达阵列

Figure 8. Ultra-wideband MIMO radar array used for data acquisition

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图 9 不同训练集随训练轮数的识别率变化

Figure 9. Recognition rate variation with training epochs for different training sets

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图 10 10类动作的混淆矩阵

Figure 10. Confusion matrix of 10 types of actions

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图 11 模型的F1, Recall, Precision参数雷达图

Figure 11. Radar charts for model’s F1, Recall, Precision parameters

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图 12 t-SNE特征嵌入可视化

Figure 12. t-SNE feature embedding visualization

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图 13 人体目标进行不同类型动作示意图

Figure 13. The human target is performing different actions

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表 1 MIMO超宽带雷达参数表

Table 1. Parameter table for ultra-wideband MIMO radar

参数	指标
信号体制	步进频信号
信号带宽	1 GHz
工作频段	1.78～2.78 GHz
信号重复频率	10 Hz
信号步进带宽	4 MHz
信号发射功率	20 dBm (100 mW)
系统尺寸	60 cm×88 cm
天线阵元数	10发10收
可穿透介质	塑料、木板、砖墙等

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表 2 不同数据集在模型中的识别率

Table 2. Recognition rates of different datasets within the model

数据集	阈值(dB)	采样点数	识别率(%)
a	–4	256	73.92
b	–8	512	85.75
c	–10	768	78.45
d	–14	1024	85.65
e	–16	2048	85.86
b+c	–8; –10	512; 768	86.55
b+d	–8; –14	512; 1024	96.75
c+d	–10; –14	768; 1024	88.75
b+c+d	–8; –10; –14	512; 768; 1024	92.25
注：加粗项表示在所有数据集中，表现出识别率最高的数据集。

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表 3 不同网络骨干对网络整体的影响

Table 3. The impact of different network backbones on the overall network performance

网络模型	Acc (%)	Params (MB)
PointNet++, GRU	81.33	1.68
PointNet++, bi-GRU	84.65	1.68
PointNet++, LSTM PointNet++, bi-LSTM	83.38 85.65	2.17 2.17
PointNet++, Multihead Attention, bi-GRU	93.50	2.54
PointNet++, Multihead Attention, bi-LSTM PointNet++, Transformer	94.63 96.75	2.54 0.37
注：加粗项表示UWB-PointTransformer网络的识别率以及网络参数量。

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表 4 不同模型的性能对比和在不同场景下的识别率

Table 4. Cross-scenario performance and recognition rates of various models

模型	S1 (%)	S2 (%)	S3 (%)	FLOPs (GB)	Params (MB)
UWB-PointTransformer	96.75	93.45	82.65	1.60	0.37
Res3D^[39]	92.25	90.00	77.00	3.25	31.69
SFN^[40]	88.00	80.50	70.25	18.27	8.58
TSN^[41]	85.75	83.50	60.75	32.28	22.34
TSM^[42]	91.50	88.00	73.75	16.48	12.71
3D-ShuffleViT^[21]	91.85	90.68	76.48	1.68	2.45
注：加粗项表示UWB-PointTransformer网络在不同场景的识别率以及网络运算量和参数量。

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表 5 网络对不同动作的预测概率

Table 5. The network’s prediction probabilities for different actions

真实动作	预测动作
真实动作	开双臂	打拳	静坐	踢腿	坐下	站立	向前走	向左走	向右走	挥手
开双臂	9.97E–1	7.78E–7	7.71E–5	1.45E–6	1.16E–5	3.43E–4	1.79E–8	3.40E–5	2.99E–5	2.60E–3
打拳	1.61E–8	9.99E–1	5.23E–9	9.07E–6	1.64E–6	1.32E–5	2.91E–8	1.04E–6	8.83E–9	2.28E–4
向前走	2.22E–5	5.29E–5	1.62E–4	1.66E–4	2.44E–5	4.05E–7	9.99E–1	7.01E–6	9.60E–9	5.47E–4
挥手	5.63E–5	4.88E–5	5.05E–8	8.70E–6	5.12E–6	1.04E–4	7.48E–7	1.86E–5	6.64E–8	9.99E–1
注：加粗项表示网络对真实动作的预测概率。

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