2.765

2022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, 2022, 48(12): 2951−2959 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, 2022, 48(12): 2951−2959 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, 2022, 48(12): 2951−2959 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, 2022, 48(12): 2951−2959 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(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. 主要研究方向为视频图像处理, 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. 主要研究方向为视频图像处理, 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. 主要研究方向为视频图像处理, 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. 主要研究方向为视频图像处理, 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. 主要研究方向为视频图像处理, 计算机视觉.E-mail: 2018282120154@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
    Author Bio:

    XIAO Jin-Sheng Ph.D., associate professor at the School of Electronic Information, Wuhan University. He received his Ph.D. degree from Wuhan University in 2001. His research interest covers video and image processing, computer vision

    SHEN Meng-Yao Master student at the School of Electronic Information, Wuhan University. She received her bachelor degree from Wuhan University in 2018. Her research interest covers video and image processing, computer vision

    JIANG Ming-Jun Master student at the School of Electronic Information, Wuhan University. He received his bachelor degree from Wuhan University in 2019. His research interest covers video and image processing, computer vision

    LEI Jun-Feng Ph.D., associate professor at the School of Electronic Information, Wuhan University. His research interest covers video and image processing, and computer vision. Corresponding author of this paper

    BAO Zhen-Yu Master student at the School of Electronic Information, Wuhan University. He received his bachelor degree from Wuhan University of Technology in 2018. His research interest covers video and image processing, and computer vision

  • 摘要: 针对监控视频中行人非正常行走状态的异常现象, 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4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
    时空自编码器[10]0.6440.380
    变分编码器[12]0.2690.706
    多实例排名[14]0.4450.488
    本文算法0.7540.292
    下载: 导出CSV

    表  2  算法处理时间(CPU) (ms)

    Table  2  The processing time of algorithms (CPU) (ms)

    时空自编码器变分编码器本文算法
    238245173
    下载: 导出CSV
  • [1] Xiao T, Zhang C, Zha H B, Wei F Y. 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. 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 Z, Sun X Y, Zha Z J, Zeng W J. 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 I, Shao L. 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 J, Chen Z R, Wang H, Yeang D Y. 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. 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, Torresani L, Paluri M. 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 W, Shi J P, Jia J Y. 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 X, Bhalodia V, Vasconcelos N. 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
图(8) / 表(2)
计量
  • 文章访问数:  1508
  • HTML全文浏览量:  643
  • PDF下载量:  281
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-11-25
  • 录用日期:  2020-03-25
  • 网络出版日期:  2022-11-24
  • 刊出日期:  2022-12-23

目录

    /

    返回文章
    返回