2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

融合包注意力机制的监控视频异常行为检测

肖进胜 申梦瑶 江明俊 雷俊峰 包振宇

肖进胜, 申梦瑶, 江明俊, 雷俊峰, 包振宇. 融合包注意力机制的监控视频异常行为检测. 自动化学报, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190805
引用本文: 肖进胜, 申梦瑶, 江明俊, 雷俊峰, 包振宇. 融合包注意力机制的监控视频异常行为检测. 自动化学报, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190805
Xiao Jin-Sheng, Shen Meng-Yao, Jiang Ming-Jun, LEI Jun-Feng, Bao Zhen-Yu. Abnormal behavior detection algorithm with video-bag attention mechanism in surveillance video. Acta Automatica Sinica, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190805
Citation: Xiao Jin-Sheng, Shen Meng-Yao, Jiang Ming-Jun, LEI Jun-Feng, Bao Zhen-Yu. Abnormal behavior detection algorithm with video-bag attention mechanism in surveillance video. Acta Automatica Sinica, 2020, 45(x): 1−9 doi: 10.16383/j.aas.c190805

融合包注意力机制的监控视频异常行为检测

doi: 10.16383/j.aas.c190805
基金项目: 国家重点研发计划项目(2016YFB0502602, 2017YFB1302401)资助
详细信息
    作者简介:

    肖进胜:博士, 武汉大学电子信息学院副教授. 2001年于武汉大学获理学博士学位. 主要研究方向: 视频图像处理, 计算机视觉.E-mail: xiaojs@whu.edu.cn

    申梦瑶:武汉大学电子信息学院硕士研究生. 2018年获得武汉大学电子信息学院工学学士学位. 主要研究方向: 视频图像处理, 计算机视觉.E-mail: shenmy@whu.edu.cn

    江明俊:武汉大学电子信息学院硕士研究生. 2019年获得武汉大学电子信息学院工学学士学位. 主要研究方向: 视频图像处理, 计算机视觉.E-mail: 2015301200236@whu.edu.cn

    雷俊峰:博士, 武汉大学电子信息学院副教授. 2002年于武汉大学获得理学博士学位. 主要研究方向: 视频图像处理, 计算机视觉. 本文通信作者.E-mail: jflei@whu.edu.cn

    包振宇:武汉大学电子信息学院硕士研究生. 2018获得武汉理工大学信息工程学院工学学士学位. 主要研究方向: 视频图像处理, 计算机视觉.E-mail: 2018282120154@whu.edu.cn

    通讯作者:

    博士, 武汉大学电子信息学院副教授. 2002年于武汉大学获得理学博士学位. 主要研究方向: 视频图像处理, 计算机视觉. 本文通信作者.E-mail: jflei@whu.edu.cn

Abnormal Behavior Detection Algorithm with Video-Bag Attention Mechanism in Surveillance Video

Funds: Supported by National Key Research and Development Program of China (2016YFB0502602, 2017YFB1302401)
More Information
    Corresponding author: LEI Jun-Feng Ph. D., associate professor at the School of Electronic Information, Wuhan University. His research interest covers video and image processing, computer vision. Corresponding author of this paper
  • 摘要: 针对监控视频中行人非正常行走状态的异常现象, 本文提出了一个端到端的异常行为检测网络, 以视频包为输入, 输出异常得分. 时空编码器提取视频包时空特征后, 利用基于隐向量的注意力机制对包级特征进行加权处理, 最后用包级池化映射出视频包得分. 本文整合了四个常用的异常行为检测数据集,在整合数据集上进行算法测试并与其他异常检测算法进行对比. 多项客观指标结果显示, 本文算法在异常事件检测方面有着显著的优势.
  • 图  1  异常行为检测网络架构

    Fig.  1  The framework for abnormal behavior detection

    图  2  融合层特征输出结果图

    Fig.  2  The feature map of fusion-layer

    图  3  视频包得分计算流程

    Fig.  3  The flowchart of Bag-score calculation

    图  4  不同预测分值下的loss变化

    Fig.  4  The loss under different predictions

    图  5  损失训练变化曲线图

    Fig.  5  The loss curve in training stage

    图  6  异常检测算法ROC曲线图

    Fig.  6  The ROC curve of different algorithms

    图  7  异常检测算法在帧级及事件级指标对比图

    Fig.  7  The frame-level and event-level index of different algorithms

    图  8  视频检测结果

    Fig.  8  The results of abnormal behavior detection in videos

    表  1  异常检测算法AUC及EER指标

    Table  1  The AUC and EER of different algorithms

    算法AUCEER
    encoder0.6440.380
    vae0.2690.706
    mir0.4450.488
    milfusion(proposed)0.7540.292
    下载: 导出CSV

    表  2  算法处理时间(CPU)

    Table  2  The processing time of algorithms (CPU)

    encodervaemilfusion(proposed)
    238 ms245 ms173 ms
    下载: 导出CSV
  • [1] Xiao T, Zhang C, Zha H, et al. Anomaly detection via local coordinate factorization and spatio-temporal pyramid. In: Proceedings of the 12th Asian Conference on Computer Vision. Singapore, Singapore: Springer, 2015. 66−82
    [2] Reddy V, Sanderson C, Lovell B C, et al. Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Colorado Springs, CO, USA: IEEE, 2011. 55−61
    [3] 肖进胜, 朱力, 赵博强, 雷俊锋, 王莉. 基于主成分分析的分块视频噪声估计. 自动化学报, 2018, 44(09): 1618−1625

    Xiao Jin-Sheng, Zhu Li, Zhao Bo-Qiang, Lei Jun-Feng, Wang Li. Block-based video noise estimation algorithm via principal component analysis. Acta Automatica Sinica, 2018, 44(09): 1618−1625
    [4] 罗会兰, 王婵娟. 行为识别中一种基于融合特征的改进VLAD编码方法. 电子学报, 2019, 47(01): 49−58 doi: 10.3969/j.issn.0372-2112.2019.01.007

    Luo Hui-Lan, Wang Chan-Juan. An improved VLAD coding method based on fusion feature in action recognition. Acta Electronica Sinica, 2019, 47(01): 49−58 doi: 10.3969/j.issn.0372-2112.2019.01.007
    [5] Xiao J, Shen M, Lei J, Zhou J, Klette R, Sui H. Single image dehazing based on learning of haze layers. Neurocomputing, 2020 doi: 10.1016/j.neucom.2020.01.007
    [6] Zhou Y, Sun X, Zha Z, et al. MiCT: mixed 3D/2D convolutional tube for human action recognition. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA: IEEE, 2018. 449−458
    [7] Ionescu R T, Khan F S, Georgescu M, et al. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019. 7834−7843
    [8] 蔡瑞初, 谢伟浩, 郝志峰, 王丽娟, 温雯. 基于多尺度时间递归神经网络的人群异常检测. 软件学报, 2015, 26(11): 2884−2896

    Cai Rui-Chu, Xie Wei-Hao, Hao Zhi-Feng, Wang Li-Juan, Wen Wen. Abnormal crowd detection based on multi-scale recurrent neural network. Journal of Software, 2015, 26(11): 2884−2896
    [9] 袁非牛, 章琳, 史劲亭, 夏雪, 李钢. 自编码神经网络理论及应用综述. 计算机学报, 2019, 42(01): 203−230

    Yuan Fei-Niu, Zhang Lin, Shi Jin-Ting, Xia Xue, Li Gang. Theories and applications of auto-encoder neural networks: a literature survey. Chinese Journal of Computers, 2019, 42(01): 203−230
    [10] Chong Y S, Tay Y H. Abnormal event detection in videos using spatiotemporal autoencoder. International Symposium on Neural Networks, 2017, (10262): 189−196
    [11] Shi X, Chen Z, Wang H, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Quebec, Canada: MIT Press, 2015. 802−810
    [12] An J, Cho S. Variational autoencoder based anomaly detection using reconstruction probability. SNU Data Mining Center, Korea, Spe. Lec. on IE, 2015, 2: 1−18
    [13] 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

    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
    [14] Sultani W, Chen C, Shah M, et al. Real-world anomaly detection in surveillance videos. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA: IEEE, 2018. 6479−6488
    [15] Tran D, Bourdev L, Fergus R, et al. Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4489−4497
    [16] 肖进胜, 周景龙, 雷俊锋, 刘恩雨, 舒成. 基于霾层学习的单幅图像去雾算法. 电子学报, 2019, 47(10): 2142−2148 doi: 10.3969/j.issn.0372-2112.2019.10.016

    Xiao Jin-Sheng, Zhou Jing-Long, Lei Jun-Feng, Liu EnYu, Shu Cheng. Single image dehazing algorithm based on the learning of hazy layers. Acta Electronica Sinica, 2019, 47(10): 2142−2148 doi: 10.3969/j.issn.0372-2112.2019.10.016
    [17] Lu C, Shi J, Jia J, et al. Abnormal event detection at 150 FPS in MATLAB. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia: IEEE, 2013. 2720−2727
    [18] Unusual crowd activity dataset of university of Minnesota [Online], available: http://mha.cs.umn.edu/Movies/Crowdctivity-All. avi, October 25, 2006
    [19] Mahadevan V, Li W, Bhalodia V, et al. Anomaly detection in crowded scenes. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010. 1975−1981
    [20] Saligrama V, Chen Z. Video anomaly detection based on local statistical aggregates. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE, 2012. 2112−2119
  • 加载中
计量
  • 文章访问数:  43
  • HTML全文浏览量:  23
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-11-25
  • 录用日期:  2020-03-25

目录

    /

    返回文章
    返回