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一种轻量型人体行为识别学习模型

南静 建中华 宁传峰 代伟

南静, 建中华, 宁传峰, 代伟. 一种轻量型人体行为识别学习模型[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.03.18.001
引用本文: 南静, 建中华, 宁传峰, 代伟. 一种轻量型人体行为识别学习模型[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.03.18.001
NAN Jing, JIAN Zhong-hua, NIGN Chuan-feng, DAI Wei. Lightweight human activity recognition learning model[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.03.18.001
Citation: NAN Jing, JIAN Zhong-hua, NIGN Chuan-feng, DAI Wei. Lightweight human activity recognition learning model[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.03.18.001

一种轻量型人体行为识别学习模型

doi: 10.13374/j.issn2095-9389.2021.03.18.001
基金项目: 国家自然科学基金面上资助项目(61973306);江苏省优秀青年基金资助项目(BK20200086)
详细信息
    通讯作者:

    E-mail: weidai@cumt.edu.cn

  • 中图分类号: TP391.4

Lightweight human activity recognition learning model

More Information
  • 摘要: 提出一种基于近邻成分分析(Neighbourhood component analysis, NCA)、L2正则化和随机配置网络(Stochastic configuration networks, SCNs)的轻量型人体行为识别学习模型. 首先, 针对人体行为特征集维数过高且可分性差的问题, 利用NCA从特征集中选择高相关性特征子集, 进而提高模型建模计算过程的轻量性和识别精度. 其次, 针对SCNs隐含层节点过多时容易出现过拟合的问题, 采用L2正则化方法增强SCNs的泛化能力, 同时利用监督机制约束产生隐含层参数的方法, 极大地提高了SCNs模型的轻量性. 最后, 将所提NCA−L2−SCNs学习模型在UCI HAR特征集上进行验证, 实验结果表明, 相比于其他模型, 本文所提轻量型模型对于人体行为识别具有更好的识别精度和更快的建模速度.

     

  • 图  1  SCNs网络结构图

    Figure  1.  SCNs network structure

    图  2  使用L2正则化前后SCNs的RMSE对比图

    Figure  2.  RMSE comparison of SCNs before and after L2 regularization

    图  3  特征权重分析图

    Figure  3.  Analysis chart of feature weight

    图  4  使用NCA前后L2−SCNs收敛曲线图

    Figure  4.  L2−SCNs convergence curve before and after NCA

    表  1  不同算法的计算复杂度对比

    Table  1.   Comparison of the computational complexity of different algorithms

    AlgorithmsComputational complexity
    SCNs $O( {{m^3} + 2 \times 561 \times m} )$
    L2−SCNs $ O( {{2 \mathord{\left/ {\vphantom {2 3}} \right. } 3}({D^3}) + 2 \times 561 \times m} ) $
    NCA−L2−SCNs $ O( {{2 \mathord{\left/ {\vphantom {2 3}} \right. } 3}({D^3}) + 2 \times 111 \times m} ) $
    下载: 导出CSV

    表  2  使用NCA特征选择前后L2−SCNs模型结果对比

    Table  2.   Comparison of L2−SCNs model results before and after using the NCA feature selection

    Feature dimensionModeling time/sAverage accuracy/%Minimum accuracy/%Maximum accuracy/%
    UCI feature set(561)29.8594.2794.1094.30
    NCA(111)18.0397.4897.0597.93
    NCA(94)19.8897.1996.6197.49
    下载: 导出CSV

    表  3  五种方法在UCI特征集上的对比

    Table  3.   Comparison of five methods on the UCI feature set

    ModelAverage accuracy/%Maximum accuracy/%Minimum accuracy/%Modeling time/s
    SVM92.7792.7792.7719.41
    LSTM93.3594.8892.57529
    SCNs94.1894.2794.0660.59
    L2−SCNs94.2794.3094.1029.85
    NCA−L2−SCNs97.4897.9397.0518.03
    下载: 导出CSV

    表  4  NCA−SCNs模型识别结果混淆矩阵

    Table  4.   Confusion matrix of NCA−SCNs model recognition results

    Predicted classActual class
    WalkingUpstairsDownstairsSittingStandingLying
    Walking494230000
    Upstairs14434400
    Downstairs02415000
    Sitting13046610
    Standing001205312
    Lying00010535
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-18
  • 网络出版日期:  2021-06-18

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