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基于CNN的监控视频事件检测

王梦来 李想 陈奇 李澜博 赵衍运

王梦来, 李想, 陈奇, 李澜博, 赵衍运. 基于CNN的监控视频事件检测. 自动化学报, 2016, 42(6): 892-903. doi: 10.16383/j.aas.2016.c150729
引用本文: 王梦来, 李想, 陈奇, 李澜博, 赵衍运. 基于CNN的监控视频事件检测. 自动化学报, 2016, 42(6): 892-903. doi: 10.16383/j.aas.2016.c150729
WANG Meng-Lai, LI Xiang, CHEN Qi, LI Lan-Bo, ZHAO Yan-Yun. Surveillance Event Detection Based on CNN. ACTA AUTOMATICA SINICA, 2016, 42(6): 892-903. doi: 10.16383/j.aas.2016.c150729
Citation: WANG Meng-Lai, LI Xiang, CHEN Qi, LI Lan-Bo, ZHAO Yan-Yun. Surveillance Event Detection Based on CNN. ACTA AUTOMATICA SINICA, 2016, 42(6): 892-903. doi: 10.16383/j.aas.2016.c150729

基于CNN的监控视频事件检测

doi: 10.16383/j.aas.2016.c150729
详细信息
    作者简介:

    李想 北京邮电大学信息与通信工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别

    陈奇 北京邮电大学信息与通信工程学院硕士研究生. 主要研究方向为计算机视觉与模式识别

    李澜博 北京邮电大学信息与通信工程学院硕士研究生. 主要研究方向为计算机视觉和大规模深度学习.

    赵衍运 北京邮电大学信息与通信工程学院副教授. 主要研究方向为计算机视觉与模式识别

    通讯作者:

    王梦来 北京邮电大学信息与通信工程学院硕士研究生. 主要研究方向为计算机视觉和深度学习. 本文通信作者. E-mail: wangmenglai@bupt.edu.cn

  • 中图分类号: 

Surveillance Event Detection Based on CNN

More Information
    Author Bio:

    (LI Xiang Master student at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His research interest covers computer vision and pattern recognition

    CHEN Qi Master student at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His research interest covers computer vision and pattern recognition

    LI Lan-Bo Master student at the School of Information and Communica- tion Engineering, Beijing University of Posts and Telecommunications. His re- search interest covers computer vision and large scale deep learning.

    ZHAO Yan-Yun Associate professor at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. Her research interest covers com- puter vision and pattern recognition

    Corresponding author: WANG Meng-Lai Master student at the School of Information and Com- munication Engineering, Beijing Uni- versity of Posts and Telecommunica- tions. His research interest covers computer vision and deep learning. Corresponding author of this paper. E-mail:wangmenglai@bupt.edu.cn
  • 摘要: 复杂监控视频中事件检测是一个具有挑战性的难题, 而TRECVID-SED评测使用的数据集取自机场的实际监控视频,以高难度著称. 针对TRECVID-SED评测集, 提出了一种基于卷积神经网络(Convolutional neural network, CNN)级联网络和轨迹分析的监控视频事件检测综合方案. 在该方案中, 引入级联CNN网络在拥挤场景中准确地检测行人, 为跟踪行人奠定了基础; 采用CNN网络检测具有关键姿态的个体事件, 引入轨迹分析方法检测群体事件. 该方案在国际评测中取得了很好的评测排名: 在6个事件检测的评测中, 3个事件检测排名第一.
  • 图  1  头肩检测的级联深度网络(HsNet)结构[26]

    Fig.  1  The architecture of the CNN cascade for head-shoulder detection[26]

    图  2  在线学习非线性运动模式及鲁棒外观模型的多目标跟踪算法框图[13]

    Fig.  2  The block diagram of multi-target tracking by online learning of non-linear motion patterns and robust appearance models[13]

    图  3  Pointing和Embrace事件样本截图

    Fig.  3  Samples of Pointing and Embrace

    图  4  4ObjectPut和PersonRuns事件样本截图

    Fig.  4  Samples of ObjectPut and PersonRuns

    图  5  ObjectPut和PersonRuns事件关键姿态检测的网络结构

    Fig.  5  The architecture of CNN for ObjectPut and PersonRuns key-pose detection

    图  6  群体事件检测框图

    Fig.  6  The block diagram of group event detection

    图  7  头肩区域训练样本示例

    Fig.  7  Samples of head-shoulder

    图  8  与当前最先进的检测方法在SED-PD上的对比[26] (用平均对数漏检率排列,越小越好)

    Fig.  8  Comparison of our results with several state-of-the-art methods on SED-PD[26] (The legends are ordered by log-average miss-rate,the lower the better.

    图  9  在SED-PD上的部分检测结果[26]

    (红框表示正确检测,蓝框表示虚检,绿框表示漏检)

    Fig.  9  Detection results on SED-PD[26]

    (red: correct detection,blue: false alarm,green: missed detection)

    图  10  高斯过程回归改进效果

    Fig.  10  The improved results of Gaussian process regression

    表  1  2015年TRECVID-SED评测结果

    Table  1  Evaluation Results of TRECVID-SED 2015

    排名其他团队最好成绩(ADCR)ADCR#Targ#CorDet#FA#Miss
    Embrace10.86800.79091383690102
    ObjectPut11.01601.0120289233287
    PeopleMeet40.89391.042625630278226
    PeopleSplitUp20.89340.938715224168128
    PersonRuns20.57680.97005048746
    Pointing11.01401.00407941642778
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-11-03
  • 录用日期:  2016-04-01
  • 刊出日期:  2016-06-20

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