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融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用

袁静 章毓晋

袁静, 章毓晋. 融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用. 自动化学报, 2017, 43(4): 604-610. doi: 10.16383/j.aas.2017.c150667
引用本文: 袁静, 章毓晋. 融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用. 自动化学报, 2017, 43(4): 604-610. doi: 10.16383/j.aas.2017.c150667
YUAN Jing, ZHANG Yu-Jin. Application of Sparse Denoising Auto Encoder Network with Gradient Difference Information for Abnormal Action Detection. ACTA AUTOMATICA SINICA, 2017, 43(4): 604-610. doi: 10.16383/j.aas.2017.c150667
Citation: YUAN Jing, ZHANG Yu-Jin. Application of Sparse Denoising Auto Encoder Network with Gradient Difference Information for Abnormal Action Detection. ACTA AUTOMATICA SINICA, 2017, 43(4): 604-610. doi: 10.16383/j.aas.2017.c150667

融合梯度差信息的稀疏去噪自编码网络在异常行为检测中的应用

doi: 10.16383/j.aas.2017.c150667
基金项目: 

国家自然科学基金 61673234

国家自然科学基金 U1636124

国家自然科学基金 61171118

详细信息
    作者简介:

    章毓晋  清华大学电子工程系教授.主要研究方向为图像处理, 图像分析, 图像理解及其技术应用.E-mail:zhangyj@tsinghua.edu.cn

    通讯作者:

    袁静  清华大学博士研究生.主要研究方向为数字图像处理技术与机器学习.E-mail:yuanjing20110824@sina.com

Application of Sparse Denoising Auto Encoder Network with Gradient Difference Information for Abnormal Action Detection

Funds: 

National Natural Science Foundation of China 61673234

National Natural Science Foundation of China U1636124

National Natural Science Foundation of China 61171118

More Information
    Author Bio:

      Professor at Tsinghua University. His research interest covers image processing, image analysis, image understanding, and their applications

    Corresponding author: YUAN Jing   Ph. D. candidate at Tsinghua University. Her research interest covers computer vision and machine learning. Corresponding author of this paper
  • 摘要: 本文是在稀疏去噪自编码网络的基础上,增加梯度差约束条件改进了自编码网络的解码效果,并成功地应用于全局异常行为检测的领域.基于稀疏自编码网络异常行为的检测过程是通过训练非异常行为的视频帧数据得到自编码网络模型,将待测视频帧输入模型,根据前向传播算法得到模型的输出,计算输出与输入之间的损失值,当该值高于某个阈值时,判定该视频帧中存在异常行为.通过在标准异常行为库开展的实验表明融合梯度差信息的稀疏去噪自编码网络算法较传统的稀疏去噪自编码网络算法在全局异常行为检测中更加有效.
  • 图  1  基于DAE自编码网络的异常行为检测的网络学习结构图

    Fig.  1  The training framework for abnormal detection with DAE network

    图  2  基于DAE自编码网络的异常行为检测的网络推理过程

    Fig.  2  The inferencing framework for abnormal detection with DAE network

    图  3  隐层节点数量对异常行为检测结果的影响

    Fig.  3  The influence of hidden-layer neuron number on detecting results

    图  4  数据重建效果

    Fig.  4  Reconstruction results

    图  5  场景3检测结果

    Fig.  5  The detection example on Scene 3

    表  1  DAE算法与DAE-GS算法的实验结果

    Table  1  Detecting results with DAE and DAE-GS algorithms

    隐层神经元
    数量 ($ {2^N} $)
    S1 (DAE-GS) S1 (DAE) S2 (DAE-GS) S2 (DAE) S3 (DAE-GS) S3 (DAE)
    40.96430.94440.99340.98980.95680.9299
    50.94460.93830.98440.99590.97550.9029
    60.93910.92290.96110.99170.94550.9639
    70.95330.95480.99000.9823(0.9867)0.9214
    80.95120.91770.99850.99130.97120.9135
    90.95410.95200.99360.99660.97200.8870
    100.95760.95160.99320.99560.98240.9319
    110.96830.93540.99150.99630.95270.9457
    120.97160.93650.99770.98690.97220.9667
    130.97340.92490.99850.99730.94740.8770
    14(0.9810)0.9242(0.9987)0.99020.98400.9607
    150.97360.94010.99820.99470.97530.9464
    下载: 导出CSV

    表  2  与当前流行算法之间的正确率的比较

    Table  2  Recognition accuracies compared with state of art methods

    算法 场景1 场景2 场景3
    Sparse $\mathit{0}\mathit{.995} $ 0.975 0.964
    Sparse + LSDS $\boldsymbol{0.9955} $ 0.971 0.974
    DAE 0.957 $ \boldsymbol{0.998} $ 0.966
    DAE-GS 0.981 $\boldsymbol{(0.998)} $ $\boldsymbol{(0.986)} $
    下载: 导出CSV

    表  3  不同算法的时间复杂性比较 (ms)

    Table  3  Comparison of difierent methods in terms of computational time (ms)

    算法 训练时间 整体测试时间 单帧测试时间
    Sparse 12 410 6 890 6.56
    DAE 12 403 7 100 6.76
    DAE-GS 20 210 9 034 8.60
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-10-30
  • 录用日期:  2016-06-12
  • 刊出日期:  2017-04-01

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