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多聚点子空间下的时空信息融合及其在行为识别中的应用

杨天金 侯振杰 李兴 梁久祯 宦娟 郑纪翔

杨天金, 侯振杰, 李兴, 梁久祯, 宦娟, 郑纪翔. 多聚点子空间下的时空信息融合及其在行为识别中的应用. 自动化学报, 2022, 48(11): 2823−2835 doi: 10.16383/j.aas.c190327
引用本文: 杨天金, 侯振杰, 李兴, 梁久祯, 宦娟, 郑纪翔. 多聚点子空间下的时空信息融合及其在行为识别中的应用. 自动化学报, 2022, 48(11): 2823−2835 doi: 10.16383/j.aas.c190327
Yang Tian-Jin, Hou Zhen-Jie, Li Xing, Liang Jiu-Zhen, Huan Juan, Zheng Ji-Xiang. Recognizing action using multi-center subspace learning-based spatial-temporal information fusion. Acta Automatica Sinica, 2022, 48(11): 2823−2835 doi: 10.16383/j.aas.c190327
Citation: Yang Tian-Jin, Hou Zhen-Jie, Li Xing, Liang Jiu-Zhen, Huan Juan, Zheng Ji-Xiang. Recognizing action using multi-center subspace learning-based spatial-temporal information fusion. Acta Automatica Sinica, 2022, 48(11): 2823−2835 doi: 10.16383/j.aas.c190327

多聚点子空间下的时空信息融合及其在行为识别中的应用

doi: 10.16383/j.aas.c190327
基金项目: 国家自然科学基金(61803050, 61063021), 江苏省物联网移动互联技术工程重点实验室开放课题基金(JSWLW-2017-013), 浙江省公益技术研究社会发展项目(2017C33223)资助
详细信息
    作者简介:

    杨天金:常州大学信息科学与工程学院硕士研究生. 主要研究方向为行为识别, 机器学习.E-mail: yangtianjin128@163.com

    侯振杰:常州大学信息科学与工程学院教授. 2015年获内蒙古农业大学机械专业博士学位. 主要研究方向为行业识别, 机器学习. 本文通信作者.E-mail: houzj@cczu.edu.cn

    李兴:常州大学信息科学与工程学院硕士研究生. 主要研究方向为行为识别, 机器学习.E-mail: lixing03201012@163.com

    梁久祯:常州大学信息科学与工程学院教授. 2001年获北京航空航天大学计算机软件与理论工学博士学位. 主要研究方向为行为识别, 机器学习.E-mail: jzliang@cczu.edu.cn

    宦娟:常州大学信息科学与工程学院副教授. 2019年获江苏大学农业电气化与自动化专业博士学位. 主要研究方向为信息智能处理.E-mail: huanjuan@cczu.edu.cn

    郑纪翔:2020年于常州大学信息科学与工程学院获得学士学位. 主要研究方向为行为识别, 机器学习.E-mail: zjx991031@163.com

Recognizing Action Using Multi-center Subspace Learning-based Spatial-temporal Information Fusion

Funds: Supported by National Natural Science Foundation of China (61803050, 61063021), Jiangsu Province Networking and Mobile Internet Technology Engineering Key Laboratory Open Research Fund Project (JSWLW-2017-013), and Zhejiang Public Welfare Technology Research Social Development Project (2017C33223)
More Information
    Author Bio:

    YANG Tian-Jin Master student at the School of Information Science and Engineering, Changzhou University. His research interest covers behavior recognition and machine learning

    HOU Zhen-Jie Professor at the School of Information Science and Engineering, Changzhou University. He received his Ph.D. degree in mechanical engineering from Inner Mongolia Agricultural University in 2015. His research interest covers behavior recognition and machine learning. Corresponding author of this paper

    LI Xing  Master student at the School of Information Science and Engineering, Changzhou University. His research interest covers behavior recognition and machine learning

    LIANG Jiu-Zhen Professor at the School Information Science and Engineering, Changzhou University. He received his Ph.D. degree in computer software and theory engineering from Beijing University of Aeronautics and Astronautics in 2001. His research interest covers behavior recognition and machine learning

    HUAN Juan Associate professor at the School of Information Science and Engineering, Changzhou University. She received her Ph.D. degree in agricultural electriflcation automation from Jiangsu University in 2019. Her main research interest is information intelligence processing

    ZHENG Ji-Xiang Received his bachelor degree from the School of Information Science and Engineering, Changzhou University in 2020. His research interest covers behavior recognition and machine learning

  • 摘要: 基于深度序列的人体行为识别, 一般通过提取特征图来提高识别精度, 但这类特征图通常存在时序信息缺失的问题. 针对上述问题, 本文提出了一种新的深度图序列表示方式, 即深度时空图(Depth space time maps, DSTM). DSTM降低了特征图的冗余度, 弥补了时序信息缺失的问题. 本文通过融合空间信息占优的深度运动图(Depth motion maps, DMM) 与时序信息占优的DSTM, 进行高精度的人体行为研究, 并提出了多聚点子空间学习(Multi-center subspace learning, MCSL)的多模态数据融合算法. 该算法为各类数据构建多个投影聚点, 以此增大样本的类间距离, 降低了投影目标区域维度. 本文在MSR-Action3D数据集和UTD-MHAD数据集上进行人体行为识别. 最后实验结果表明, 本文方法相较于现有人体行为识别方法有着较高的识别率.
  • 图  1  DSTM流程图

    Fig.  1  DSTM flowchart

    图  2  单聚点子空间学习

    Fig.  2  Subspace learning

    图  3  多聚点子空间学习

    Fig.  3  Multi-center subspace learning

    图  4  正反高抛动作

    Fig.  4  Positive and negative high throwing action

    图  5  参数选择

    Fig.  5  The parameter of selection

    图  6  DSTM在不同分类器识别效果

    Fig.  6  DSTM recognition of different classifiers

    图  7  混淆矩阵

    Fig.  7  Confusion matrix

    表  1  MSR数据库中的人体行为

    Table  1  Human actions in MSR

    动作 样本数 动作 样本数
    高挥手 (A01) 27 双手挥 (A11) 30
    水平挥手 (A02) 26 侧边拳击 (A12) 30
    锤 (A03) 27 弯曲 (A13) 27
    手抓 (A04) 25 向前踢 (A14) 29
    打拳 (A05 26 侧踢 (A15) 20
    高抛 (A06) 26 慢跑 (A16) 30
    画叉 (A07) 27 网球挥拍 (A17) 30
    画勾 (A08) 30 发网球 (A18) 30
    画圆 (A09) 30 高尔夫挥杆 (A19) 30
    拍手 (A10) 30 捡起扔 (A20) 27
    下载: 导出CSV

    表  2  UTD数据库中的人体行为

    Table  2  Human actions in UTD

    动作 样本数 动作 样本数
    向左滑动 (B01) 32 挥网球 (B15) 32
    向右滑动 (B02) 32 手臂卷曲 (B16) 32
    挥手 (B03) 32 网球发球 (B17) 32
    鼓掌 (B04) 32 推 (B18) 32
    扔 (B05) 32 敲 (B19) 32
    双手交叉 (B06) 32 抓 (B20) 32
    拍篮球 (B07) 32 捡起扔 (B21) 32
    画叉 (B08) 31 慢跑 (B22) 31
    画圆 (B09) 32 走 (B23) 32
    持续画圆 (B10) 32 坐下 (B24) 32
    画三角 (B11) 32 站起来 (B25) 32
    打保龄球 (B12) 32 弓步 (B26) 32
    冲拳 (B13) 32 蹲 (B27) 32
    挥羽毛球 (B14) 32
    下载: 导出CSV

    表  3  MSR-Action3D 数据分组

    Table  3  MSR-Action3D data grouping

    AS1 AS2 AS3
    A02 A01 A06
    A03 A04 A14
    A05 A07 A15
    A06 A08 A16
    A10 A09 A17
    A13 A11 A18
    A18 A14 A19
    A20 A12 A20
    下载: 导出CSV

    表  4  MSR数据库上不同特征的识别率(%)

    Table  4  Different of feature action recognition on MSR (%)

    方法 测试 1 测试 2 测试 3
    AS1 AS2 AS3 均值 AS1 AS2 AS3 均值 AS1 AS2 AS3 均值
    MEI-HOG 69.79 77.63 79.72 75.71 84.00 89.58 93.24 88.94 86.95 86.95 95.45 89.78
    MEI-LBP 57.05 56.58 64.19 59.27 66.66 69.79 78.37 71.61 69.56 73.91 77.27 73.58
    DSTM-HOG 83.22 71.71 87.83 80.92 94.66 84.37 88.23 89.80 91.30 82.61 95.95 89.95
    DSTM-LBP 84.56 71.71 87.83 81.37 88.00 82.29 95.94 88.74 86.96 82.61 95.45 88.34
    MHI-HOG 69.79 72.36 70.95 71.03 88.00 84.37 89.19 87.19 95.65 82.60 95.45 91.23
    MHI-LBP 51.67 60.52 54.05 55.41 73.33 70.83 78.37 74.18 82.60 65.21 72.72 73.51
    DMM-HOG 88.00 87.78 87.16 87.65 94.66 87.78 100.00 94.15 100.00 88.23 95.45 94.56
    DMM-LBP 89.52 87.78 93.20 90.17 93.11 85.19 100.00 92.77 94.03 88.98 92.38 91.80
    下载: 导出CSV

    表  5  UTD数据库上不同特征的识别率(%)

    Table  5  Different of feature action recognition on UTD (%)

    方法 测试 1 测试 2 测试 3
    MEI-HOG 69.51 65.42 68.20
    MEI-LBP 45.12 51.97 52.61
    DSTM-HOG 71.08 80.28 89.54
    DSTM-LBP 68.81 80.97 86.06
    MHI-HOG 56.44 66.58 73.14
    MHI-LBP 49.82 53.82 57.40
    DMM-HOG 78.39 75.40 87.94
    DMM-LBP 68.98 74.94 86.75
    下载: 导出CSV

    表  6  DMM和DSTM对比实验结果(%)

    Table  6  Experimental results of DMM and DSTM (%)

    方法 D1 D2
    DSTM 62.83 81.53
    DMM 32.17 63.93
    下载: 导出CSV

    表  7  DMM和DSTM平均处理时间(s)

    Table  7  Average processing time of DMM and DSTM (s)

    方法 D1 D2
    DSTM 2.1059 3.4376
    DMM 5.6014 8.6583
    下载: 导出CSV

    表  8  $ \mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{1} $上的实验结果

    Table  8  Experimental results on $ \mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{1} $

    方法 识别率 (%)
    文献 [12] 86.50
    文献 [34] 91.45
    文献 [35] 90.01
    文献 [36] 89.40
    文献 [37] 77.47
    文献 [38] 81.7
    文献 [39] 90.01
    文献 [40] 89.48
    本文学习方法 90.32
    $注 :\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{1}$采用设置 2 测试 2.
    下载: 导出CSV

    表  9  $\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{2}$上的实验结果

    Table  9  Experimental results on $\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{2}$

    方法 识别率 (%)
    MHI-LBP 68.75
    MEI-LBP 71.43
    DCA[22] 94.64
    DSTM-LBP 87.50
    DSTM-HOG 89.28
    MCSL+DMM 89.28
    MCSL+DSTM 91.96
    CCA[21] 83.05
    子空间学习 92.85
    本文学习方法 98.21
    注: $\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{2}$采用设置 2 测试 4; MCSL 为多聚点子空间学习.
    下载: 导出CSV

    表  10  UTD-MHAD在设置2测试4上的实验结果

    Table  10  Experimental results on UTD-MHAD

    方法 识别率 (%)
    MHI-LBP 62.40
    MEI-LBP 57.80
    DCA[22] 92.48
    DSTM-LBP 89.59
    DSTM-HOG 91.90
    MCSL+DMM 93.64
    MCSL+DSTM 95.37
    CCA[21] 87.28
    子空间学习 93.64
    本文学习方法 98.84
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-04-30
  • 录用日期:  2019-11-15
  • 网络出版日期:  2022-09-14
  • 刊出日期:  2022-11-22

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