基于DenseNet和卷积注意力模块的高精度手势识别

赵雅琴; 宋雨晴; 吴晗; 何胜阳; 刘璞秋; 吴龙文

doi:10.11999/JEIT230165

基于DenseNet和卷积注意力模块的高精度手势识别

doi: 10.11999/JEIT230165

1.
哈尔滨工业大学电子与信息工程学院哈尔滨 150001
2.
中国航天科工集团八五一一研究所南京 211100

基金项目: 国家自然科学基金(61671185, 62071153)

详细信息

作者简介:
赵雅琴：女，博士，教授，研究方向为辐射源识别、辐射源个体识别、无源定位、光通信、医学信号处理等

宋雨晴：女，硕士生，研究方向为时频分析、信号处理和毫米波雷达等

吴晗：男，硕士生，研究方向为时频分析、信号处理和毫米波雷达等

何胜阳：男，博士，高级工程师，研究方向为无线光通信、嵌入式系统和算法加速等

刘璞秋：男，硕士，研究方向为人工智能、时频分析、信号处理和毫米波雷达等

吴龙文：男，博士，工程师，研究方向为辐射源识别、辐射源个体识别、无源定位、多核学习和医学信号处理等

通讯作者:
吴龙文　wulongwen@hit.edu.cn

中图分类号: TN957.51
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出版历程
- 收稿日期: 2023-03-16
- 修回日期: 2023-09-07
- 网络出版日期: 2023-09-12
- 刊出日期: 2024-03-27

High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module

1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
China Aerospace Science & Industry Corporation 8511 company, Nanjing 211100, China

Funds: The National Natural Science Foundation of China (61671185, 62071153)

摘要

摘要: 非接触的手势识别是一种新型人机交互方式，在增强现实(AR)/虚拟现实(VR)、智能家居、智能医疗等方面有着广阔的应用前景，近年来成为一个研究热点。由于需要利用毫米波雷达进行更精确的微动手势识别，该文提出一种新型的基于MIMO毫米波雷达的微动手势识别方法。采用4片AWR1243雷达板级联而成的毫米波级联(MMWCAS)雷达采集手势回波，对手势回波进行时频分析，基于距离-多普勒(RD)图和3D点云检测出人手目标。通过数据预处理，提取手势目标的距离-时间谱图(RTM)、多普勒-时间谱图(DTM)、方位角-时间谱图(ATM)和俯仰角-时间谱图(ETM)，更加全面地表征手势的运动特征，并形成混合特征谱图(FTM)，对12种微动手势进行识别。设计了基于DenseNet和卷积注意力模块的手势识别网络，将混合特征谱图作为网络的输入，创新性地融合了卷积注意力模块(CBAM)，实验表明，识别准确率达到99.03%，且该网络将注意力放在手势动作的前半段，实现了高精度的手势识别。
- 手势识别 /
- 毫米波雷达 /
- 卷积神经网络 /
- 卷积注意力模块
Abstract: Non-contact gesture recognition is a new type of human-computer interaction method with broad application prospects. It can be used in Augmented Reality (AR)/Virtual Reality (VR), smart homes, smart medical, etc., and has recently become a research hotspot. Motivated by the need for more precise micro-motion gesture recognition using mm-wave radar in recent years, a novel micro-motion gesture recognition method based on MIMO millimeter wave radar is proposed in this paper. The MilliMeter Wave CAScaded (MMWCAS) radar cascaded with four AWR1243 radar boards is used to collect gesture echoes. Time-frequency analysis is performed on gesture echoes, and the hand target is detected based on the Range-Doppler (RD) map and 3D point cloud. Through data pre-processing, the Range-Time Map (RTM), Doppler-Time Map (DTM), Azimuth-Time Map (ATM) and Elevation-Time Map (ETM) of the gestures are extracted to more comprehensively characterize the motion of the hand gesture. The mixed Feature-Time Maps (FTM) are formed and adopted for the recognition of 12 types of micro-motion gestures. An innovative gesture recognition network based on DenseNet and Convolutional Block Attention Module (CBAM) is designed, and the mixed FTM is used as the input of the network. Experimental results show that the recognition accuracy reaches 99.03%, achieving high-accuracy gesture recognition. It is discovered that the network focuses on the first half of the gesture movement, which improves the recognition accuracy.
- Gesture recognition /
- Millimeter wave radar /
- Convolutional Neural Network(CNN) /
- Convolutional Block Attention Module(CBAM)

HTML全文

图 1 人手和雷达的相对位置示意图

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图 2 本文提出的手势识别方法示意图

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图 3 特征图谱提取过程

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图 4 所提手势识别网络的整体结构

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图 5 12种手势示意图

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图 6 12种手势的DTM示例

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图 7 12种手势的ATM示例

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图 8 12种手势的ETM示例

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图 9 所提网络的混淆矩阵

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图 10 注意力分布热图

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表 1 毫米波雷达参数设置

参数名称	值	参数名称	值
发射天线数	12	调频带宽	5 GHz
接收天线数	16	调频斜率	125 MHz/µs
采集帧数	28	ADC采样率	4000 ksps
每帧chirp数	128	帧周期	72 ms
每chirp采样数	128

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表 2 实验平台

属性	型号/参数
CPU	R5-4600H
GPU	GTX1660TI
内存	16 GB
操作系统	Windows10 64 bit

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表 3 各种CNN模型进行数据扩充的效果对比

		VGG16	ResNet50	ResNet101	DenseNet121	DenseNet161
原始(%)		96.57	79.21	95.09	97.36	97.68
数据扩充之后(%)		97.08	91.76	97.45	98.15	98.29
计算复杂度	单个batch计算量	5,509,133,824	1,558,820,864	3,082,369,024	956,748,800	2,550,592,320
计算复杂度	网络参数个数	134,308,556	23,526,412	42,518,540	7,972,648	28,671,688
数据扩充后的单次迭代用时(s)		73.44	48.25	74.67	60.02	149.58

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表 4 CBAM在DenseNet121不同位置的效果对比(%)

位置	0000	0001	0010	0011	0100	0101	0110	0111
识别率	98.15	97.87	99.03	98.47	98.80	98.47	98.89	97.96

位置	1000	1001	1010	1011	1100	1101	1110	1111
识别率	98.93	98.19	98.80	97.45	97.69	97.77	98.28	97.91

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