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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

赵雅琴, 宋雨晴, 吴晗, 何胜阳, 刘璞秋, 吴龙文. 基于DenseNet和卷积注意力模块的高精度手势识别[J]. 电子与信息学报, 2024, 46(3): 967-976. doi: 10.11999/JEIT230165
引用本文: 赵雅琴, 宋雨晴, 吴晗, 何胜阳, 刘璞秋, 吴龙文. 基于DenseNet和卷积注意力模块的高精度手势识别[J]. 电子与信息学报, 2024, 46(3): 967-976. doi: 10.11999/JEIT230165
ZHAO Yaqin, SONG Yuqing, WU Han, HE Shengyang, LIU Puqiu, WU Longwen. High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module[J]. Journal of Electronics & Information Technology, 2024, 46(3): 967-976. doi: 10.11999/JEIT230165
Citation: ZHAO Yaqin, SONG Yuqing, WU Han, HE Shengyang, LIU Puqiu, WU Longwen. High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module[J]. Journal of Electronics & Information Technology, 2024, 46(3): 967-976. 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
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

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

Funds: The National Natural Science Foundation of China (61671185, 62071153)
  • 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。
  • 图  1  人手和雷达的相对位置示意图

    图  2  本文提出的手势识别方法示意图

    图  3  特征图谱提取过程

    图  4  所提手势识别网络的整体结构

    图  5  12种手势示意图

    图  6  12种手势的DTM示例

    图  7  12种手势的ATM示例

    图  8  12种手势的ETM示例

    图  9  所提网络的混淆矩阵

    图  10  注意力分布热图

    表  1  毫米波雷达参数设置

    参数名称 参数名称
    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28ADC采样率4000 ksps
    每帧chirp数128帧周期72 ms
    每chirp采样数128
    下载: 导出CSV

    表  2  实验平台

    属性型号/参数
    CPUR5-4600H
    GPUGTX1660TI
    内存16 GB
    操作系统Windows10 64 bit
    下载: 导出CSV

    表  3  各种CNN模型进行数据扩充的效果对比

    VGG16ResNet50ResNet101DenseNet121DenseNet161
    原始(%)96.5779.2195.0997.3697.68
    数据扩充之后(%)97.0891.7697.4598.1598.29
    计算复杂度单个batch计算量5,509,133,8241,558,820,8643,082,369,024956,748,8002,550,592,320
    网络参数个数134,308,55623,526,41242,518,5407,972,64828,671,688
    数据扩充后的单次迭代用时(s)73.4448.2574.6760.02149.58
    下载: 导出CSV

    表  4  CBAM在DenseNet121不同位置的效果对比(%)

    位置00000001001000110100010101100111
    识别率98.1597.8799.0398.4798.8098.4798.8997.96
    位置10001001101010111100110111101111
    识别率98.9398.1998.8097.4597.6997.7798.2897.91
    下载: 导出CSV
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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

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