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智能人机交互中第一视角手势表达的一次性学习分类识别

鹿智 秦世引 李连伟 张鼎豪

鹿智, 秦世引, 李连伟, 张鼎豪. 智能人机交互中第一视角手势表达的一次性学习分类识别. 自动化学报, 2021, 47(6): 1284−1301 doi: 10.16383/j.aas.c190754
引用本文: 鹿智, 秦世引, 李连伟, 张鼎豪. 智能人机交互中第一视角手势表达的一次性学习分类识别. 自动化学报, 2021, 47(6): 1284−1301 doi: 10.16383/j.aas.c190754
Lu Zhi, Qin Shi-Yin, Li Lian-Wei, Zhang Ding-Hao. One-shot learning classification and recognition of gesture expression from the egocentric viewpoint in intelligent human-computer interaction. Acta Automatica Sinica, 2021, 47(6): 1284−1301 doi: 10.16383/j.aas.c190754
Citation: Lu Zhi, Qin Shi-Yin, Li Lian-Wei, Zhang Ding-Hao. One-shot learning classification and recognition of gesture expression from the egocentric viewpoint in intelligent human-computer interaction. Acta Automatica Sinica, 2021, 47(6): 1284−1301 doi: 10.16383/j.aas.c190754

智能人机交互中第一视角手势表达的一次性学习分类识别

doi: 10.16383/j.aas.c190754
基金项目: 国家自然科学基金重点项目(61731001)资助
详细信息
    作者简介:

    鹿智:北京航空航天大学自动化科学与电气工程学院博士研究生. 2016年获得北京信息控制研究所计算机科学与技术硕士学位. 主要研究方向为机器视觉和模式识别.E-mail: by1603117@buaa.edu.cn

    秦世引:北京航空航天大学自动化科学与电气工程学院教授和东莞理工学院电子工程与智能化学院教授. 1990年获得浙江大学工业控制工程与智能自动化博士学位. 主要研究方向为模式识别与机器学习, 图像处理与计算机视觉, 人工智能及其应用. 本文通信作者. E-mail: qsy@buaa.edu.cn

    李连伟:北京航空航天大学自动化科学与电气工程学院博士研究生. 2017年获得山东大学控制科学与工程学院学士学位. 主要研究方向为深度学习和计算机视觉.E-mail: llw2017@buaa.edu.cn

    张鼎豪:北京航空航天大学电子信息工程学院硕士研究生. 2018年获得北京航空航天大学自动化学士学位. 主要研究方向为计算机视觉和模式识别. E-mail: hbhszdh@buaa.edu.cn

One-shot Learning Classification and Recognition of Gesture Expression From the Egocentric Viewpoint in Intelligent Human-computer Interaction

Funds: Supported by Key Projects of National Natural Science Foundation of China (61731001)
More Information
    Author Bio:

    LU Zhi Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. He received his master degree in computer science and technology from Beijing Institute of Information Control in 2016. His research interest covers machine vision and pattern recognition

    QIN Shi-Yin Professor at the School of Automation Science and Electrical Engineering, Beihang University, also at the School of Electrical Engineering and Intelligentization, Dongguan University of Technology. He received his Ph.D. degree in industrial control engineering and intelligent automation from Zhejiang University in 1990. His research interest covers pattern recognition and machine learning, image processing and computer vision, and artificial intelligence and its applications. Corresponding author of this paper

    LI Lian-Wei Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. He received his bachelor degree from the School of Control Science and Engineering, Shandong University in 2017. His research interest covers deep learning and computer vision

    ZHANG Ding-Hao Master student at the School of Electronic Information Engineering, Beihang University. He received his bachelor degree in automation from Beihang University in 2018. His research interest covers computer vision and pattern recognition

  • 摘要: 在智能人机交互中, 以交互人的视角为第一视角的手势表达发挥着重要作用, 而面向第一视角的手势识别则成为最重要的技术环节. 本文通过深度卷积神经网络的级联组合, 研究复杂应用场景中第一视角下的一次性学习手势识别(One-shot learning hand gesture recognition, OSLHGR)算法. 考虑到实际应用的便捷性和适用性, 运用改进的轻量级SSD (Single shot multibox detector)目标检测网络实现第一视角下手势目标的快速精确检测; 进而, 以改进的轻量级U-Net网络为主要工具进行复杂背景下手势目标的像素级高效精准分割. 在此基础上, 以组合式3D深度神经网络为工具, 研究提出了一种第一视角下的一次性学习手势动作识别的网络化算法. 在Pascal VOC 2012数据集和SoftKinetic DS325采集的手势数据集上进行的一系列实验测试结果表明, 本文所提出的网络化算法在手势目标检测与分割精度、分类识别准确率和实时性等方面都有显著的优势, 可为在复杂应用环境下实现便捷式高性能智能人机交互提供可靠的技术支持.
  • 图  1  不同场景下第一视角手势人机交互图示

    Fig.  1  HCI demonstration of gestures from the egocentric viewpoint in different scenarios

    图  2  第一视角下智能人机交互的活动区域图示

    Fig.  2  Demonstration of active area of intelligent HCI from the egocentric viewpoint

    图  3  改进的SSD目标检测网络架构

    Fig.  3  The network architecture of improved SSD object detection

    图  4  第一视角下手势样本数据的标注结果

    Fig.  4  Annotation results of gesture samples from the egocentric viewpoint

    图  5  迁移强化训练和随机初始化两种方式下损失函数变化曲线对比

    Fig.  5  Comparison of loss function change curves between transfer reinforcement training and random initialization

    图  6  第一视角下改进SSD目标检测网络的检测结果

    Fig.  6  The detection results of improved SSD target detection network from the egocentric viewpoint

    图  7  第一视角下手势目标检测结果的召回率−精确率变化曲线

    Fig.  7  Recall and precision curves of gesture target detection results from the egocentric viewpoint

    图  8  改进的轻量级U-Net网络结构

    Fig.  8  Improved lightweight U-Net network architecture

    图  9  第一视角下手势目标轮廓的人工标注结果

    Fig.  9  Manual annotation results of gesture target contours from the egocentric viewpoint

    图  10  基于改进U-Net的手势目标快速分割和提取算法系统架构

    Fig.  10  Architecture of fast segmentation and extraction algorithm of gesture targets based on improved U-Net

    图  11  第一视角下改进U-Net网络模型的分割结果

    Fig.  11  The segmentation results of improved U-Net network model from the egocentric viewpoint

    图  12  第一视角下手势目标分割结果的召回率−精确率变化曲线

    Fig.  12  Recall and precision curves of gesture target segmentation results from the egocentric viewpoint

    图  13  本文提出的SSD + U-Net组合方法与Mask R-CNN v3检测和分割结果对比

    Fig.  13  Comparison of detection and segmentation results between SSD + U-Net and Mask R-CNN v3

    图  14  5-way 1-shot 3D关系神经网络系统架构

    Fig.  14  5-way 1-shot 3D relation neural network system architecture

    图  15  OSLHGR任务的数据集划分图示

    Fig.  15  Illustration of dataset partitioning for OSLHGR tasks

    图  16  数据采集实验平台

    Fig.  16  Experimental platform for data collection

    图  17  10种用于验证OSLHGR算法性能的动态手势数据集. 每一列从上向下表示手势核心阶段从起始到结束的变化过程. 图中箭头用于描述动态手势运动的方向

    Fig.  17  Ten dynamic gesture datasets to verify the classification performance of OSLHGR algorithm. From top to bottom, each column represents the change process from the beginning to the end of the core phase of gestures. The arrows are used to describe the motion direction of dynamic gestures

    表  1  轻量级目标检测模型在VOC 2007测试集上的检测结果对比 ($ \dagger$表示引用文献[34]中的实验结果)

    Table  1  Comparison of detection results of lightweight target detection model on VOC 2007 test set ($ \dagger$ represents the experimental results in [34])

    目标检测算法输入图像大小训练数据集测试数据集${\rm{mAP} }$(%)计算复杂度 (M)参数量 (M)
    Tiny YOLO$\dagger$416$\times$4162007 + 2012200757.1697015.12
    Tiny SSD[35]300$\times$3002007 + 2012200761.3571 1.13
    SqueezeNet-SSD$\dagger$300$\times$3002007 + 2012200764.31180 5.50
    MobileNet-SSD$\dagger$300$\times$3002007 + 2012200768.01140 5.50
    Fire SSD[36]300$\times$3002007 + 2012200770.52670 7.13
    Pelee[37]300$\times$3002007 + 2012200770.91 210 5.98
    Tiny DSOD[34]300$\times$3002007 + 2012200772.11060 0.95
    SSD300$\times$3002007 + 2012200774.33436034.30
    改进的 SSD300$\times$3002007 + 2012200773.6710 1.64
    下载: 导出CSV

    表  2  不同网络模型分割结果和模型参数对比

    Table  2  Comparison of segmentation results and model parameters of different network models

    分割网络
    模型
    输入图像
    大小
    IoU (%)计算复
    杂度 (G)
    参数量(M)计算时间(ms/f)
    U-Net224$\times$22494.2941.8431.0367.50
    MultiResUNet224$\times$22494.0113.236.24119.50
    M2U-Net224$\times$22493.940.380.5536.25
    改进的U-Net224$\times$22494.530.520.6153.75
    下载: 导出CSV

    表  3  本文提出的目标检测和分割方法与Mask R-CNN v3的性能对比

    Table  3  Performance comparison of the proposed object detection and segmentation method and Mask R-CNN v3

    检测与分割算法输入图像大小参数量 (M)${\rm{mAP} }$(%)IoU (%)目标检测时间 (ms/f)
    Mask R-CNN v3 (ResNet-101)300$\times$30021.2098.0083.45219.73
    改进的SSD+U-Net (MobileNetV2)300$\times$3002.2596.3094.5331.04
    下载: 导出CSV

    表  4  OSLHGR算法的分类结果和模型性能对比

    Table  4  Comparison of classification results and model performance of OSLHGR algorithms

    分类算法图像类型5-way 1-shot准确率 (%)10-way 1-shot准确率 (%)参数量 (M)测试时间 (ms/f)
    C3D + Softmax[42]原始图像86.4082.7028.697.88
    手势目标分割图像91.5084.95
    Lightweight I3D + NN[43]原始图像80.7074.642.942.85
    手势目标分割图像96.1694.24
    本文算法原始图像89.4473.522.081.04
    手势目标分割图像94.6487.20
    下载: 导出CSV
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  • 收稿日期:  2019-10-31
  • 录用日期:  2020-01-17
  • 网络出版日期:  2021-06-10
  • 刊出日期:  2021-06-10

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