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基于贝叶斯生成对抗网络的背景消减算法

郑文博 王坤峰 王飞跃

郑文博, 王坤峰, 王飞跃. 基于贝叶斯生成对抗网络的背景消减算法. 自动化学报, 2018, 44(5): 878-890. doi: 10.16383/j.aas.2018.c170562
引用本文: 郑文博, 王坤峰, 王飞跃. 基于贝叶斯生成对抗网络的背景消减算法. 自动化学报, 2018, 44(5): 878-890. doi: 10.16383/j.aas.2018.c170562
ZHENG Wen-Bo, WANG Kun-Feng, WANG Fei-Yue. Background Subtraction Algorithm With Bayesian Generative Adversarial Networks. ACTA AUTOMATICA SINICA, 2018, 44(5): 878-890. doi: 10.16383/j.aas.2018.c170562
Citation: ZHENG Wen-Bo, WANG Kun-Feng, WANG Fei-Yue. Background Subtraction Algorithm With Bayesian Generative Adversarial Networks. ACTA AUTOMATICA SINICA, 2018, 44(5): 878-890. doi: 10.16383/j.aas.2018.c170562

基于贝叶斯生成对抗网络的背景消减算法

doi: 10.16383/j.aas.2018.c170562
基金项目: 

国家自然科学基金 91720000

国家自然科学基金 61533019

详细信息
    作者简介:

    郑文博  西安交通大学软件学院长学制研究生.主要研究方向为平行视觉, 平行学习, 机器学习.E-mail:zwb2017@stu.xjtu.edu.cn

    王坤峰  中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.主要研究方向为智能交通系统, 智能视觉计算, 机器学习.E-mail:kunfeng.wang@ia.ac.cn

    通讯作者:

    王飞跃  中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科技大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn

Background Subtraction Algorithm With Bayesian Generative Adversarial Networks

Funds: 

National Natural Science Foundation of China 91720000

National Natural Science Foundation of China 61533019

More Information
    Author Bio:

     Master-doctor combined candidate at the School of Software Engineering, Xi0an Jiaotong University. His research interest covers parallel vision, parallel learning, and machine learning

     Associate professor at The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers intelligent transportation systems, intelligent vision computing, and machine learning

    Corresponding author: WANG Fei-Yue  Professor at The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper
  • 摘要: 背景消减是计算机视觉和模式识别的关键技术之一.本文提出一种新的背景消减算法,该算法首先利用中值滤波算法进行背景数据的获取,然后基于贝叶斯生成对抗网络进行训练,利用生成对抗网络的特性,有效地对每个像素进行分类,解决了光照渐变和突变、非静止背景以及鬼影的问题.本文采用深度卷积神经网络,来构建贝叶斯生成对抗网络的生成器和判别器.实验结果表明,本文提出的算法性能在绝大多数情况下优于现有其他算法.本文的贡献在于首次将贝叶斯生成对抗网络应用于背景消减,并且取得了良好的实验效果.
    1)  本文责任编委 李力
  • 图  1  本文算法流程图

    Fig.  1  The flow chart of our algorithm

    图  2  基于DCGAN的贝叶斯生成对抗网络

    Fig.  2  Bayesian generative adversarial networks based on the DCGAN

    图  3  本文算法工作示意图

    Fig.  3  The flow chart of our algorithm works

    图  4  贝叶斯卷积生成对抗网络结构

    Fig.  4  The structure of Bayesian convolutional generative adversarial network

    图  5  以office训练时的贝叶斯生成对抗网络损失函数图

    Fig.  5  The loss function of the Bayesian generative adversarial networks trained base on the office datasets

    图  6  背景重建与背景减除结果图

    Fig.  6  The results of the background reconstruction and background subtraction

    图  7  背景减法算法结果对比图

    Fig.  7  Background subtraction algorithm results in comparison

    表  1  不同检测算法的召回率对比

    Table  1  The recall rate of different detection algorithms are compared

    Method GMM-Stauffer GMM-Zivkovic LBSP IUTIS MBS FTSG LFGMM LFVBGM Arun Varghese BMOG DeepBS Share Model SSOBS WeSamBE Cascade CNN BSGAN
    database Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%)
    Baseline 81.8 80.9 89.6 97.1 91.6 95.1 94.1 95.2 94.1 85.5 94.2 95.4 49.1 94.5 99.9 99.1
    Dynamic background 83.8 80.2 76.7 87.8 76.4 86.9 77.7 88.7 88.7 90.1 85.4 75.9 52.3 67.9 84.8 98.8
    Camera jitter 73.3 69.0 67.4 79.2 83.2 77.2 82.4 80.4 78.4 83.6 87.9 79.6 58.1 77.8 91.4 99.1
    Shadow 79.6 77.7 87.8 94.8 79.2 92.1 94.2 92.7 74.8 50.9 57.4 71.8 21.6 74.7 96.6 91.4
    Inter.ob.motion 51.4 54.7 55.9 69.9 75.3 76.2 81.4 72.0 91.7 85.9 95.8 94.5 50.2 94.0 93.0 98.1
    Thermal 56.9 55.4 81.4 78.3 81.6 73.6 81.6 85.0 85.1 52.4 66.3 86.2 30.1 77.2 98.9 95.3
    Bad weather 71.8 68.6 70.4 74.8 83.4 74.6 82.1 78.8 71.8 76.4 75.2 84.3 58.2 81.7 97.9 93.4
    Low frame-rate 58.2 53.0 59.7 82.1 67.7 75.2 85.4 81.4 77.3 63.8 59.2 84.3 53.1 88.4 96.4 86.1
    Night videos 52.6 48.0 51.0 56.6 55.4 61.1 65.7 66.1 36.1 64.9 53.2 59.9 44.7 63.7 94.2 91.4
    PTZ 64.8 61.1 54.8 66.4 59.7 67.3 83.1 87.8 69.8 76.7 74.6 79.7 68.8 81.5 96.2 96.8
    Air turbulence 79.1 77.9 76.1 68.6 60.4 61.1 80.5 81.2 81.2 68.7 79.8 79.1 74.4 71.8 96.1 93.1
    Average 68.5 66.0 70.1 77.8 74.0 76.4 82.6 82.7 77.2 72.6 75.4 81.0 51.0 79.4 95.0 94.8
    下载: 导出CSV

    表  2  不同检测算法的精确率对比

    Table  2  The precision rate of different detection algorithms are compared

    Method GMM-Stauffer GMM-Zivkovic LBSP IUTIS MBS FTSG LFGMM LFVBGM Arun Varghese BMOG DeepBS Share Model SSOBS WeSamBE Cascade CNN BSGAN
    database Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%)
    Baseline 84.6 89.9 95.6 93.9 94.3 91.7 93.3 95.0 93.9 81.9 96.6 95.0 94.2 91.7 97.8 98.8
    Dynamic background 59.9 62.1 59.2 92.8 86.5 91.3 89.2 90.4 90.4 75.8 90.8 91.9 10.1 89.3 96.7 96.8
    Camera jitter 51.3 48.7 83.7 85.2 84.4 76.5 81.2 86.6 86.6 72.9 93.1 83.8 34.1 83.9 96.3 97.5
    Shadow 71.6 72.3 87.7 85.8 82.6 85.3 86.5 87.1 83.9 68.2 82.5 75.8 65.3 78.8 78.2 98.8
    Inter.ob.motion 66.9 64.4 71.0 81.5 74.2 78.1 65.8 74.9 48.2 53.7 47.4 59.3 36.9 55.3 94.4 90.4
    Thermal 86.5 87.1 75.8 89.2 82.7 90.9 83.3 82.8 82.8 90.1 92.6 80.7 72.8 85.6 85.7 87.2
    Bad weather 77.0 81.4 86.6 89.6 78.3 92.3 90.9 94.7 94.8 81.5 96.8 85.7 85.1 91.3 95.5 95.9
    Low frame-rate 68.9 66.9 65.8 70.0 60.0 65.5 67.4 69.7 64.1 69.5 70.1 68.4 64.4 91.3 82.8 83.8
    Night videos 41.3 42.3 44.9 51.3 49.0 49.0 53.4 55.4 65.4 46.1 83.7 58.5 51.5 58.3 88.1 88.0
    PTZ 11.9 68.3 20.4 34.7 54.0 28.6 28.4 30.3 47.2 20.9 28.5 31.2 10.2 31.2 87.3 88.6
    Air turbulence 42.9 34.9 59.7 92.6 62.0 90.4 78.1 78.4 68.1 76.8 90.8 75.6 9.8 83.7 89.3 89.5
    Average 60.3 65.3 68.2 78.8 73.5 76.3 74.3 76.8 75.0 67.0 79.4 73.3 48.6 76.4 90.2 92.3
    下载: 导出CSV

    表  3  不同检测算法的F-measure

    Table  3  F-measure of different detection algorithms

    Method GMM-1 GMM-2 LBSP IUTIS MBS FTSG LFGMM LFVBGM Arun Varghese BMOG DeepBS Share Model SSOBS WeSamBE Cascade CNN BSGAN
    database Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%) Re (%)
    Baseline 83.2 85.2 92.5 95.5 92.9 93.4 93.7 95.1 93.9 83.0 95.1 95.2 60.8 93.1 97.8 98.1
    Dynamic background 69.9 70.0 66.8 90.2 81.1 89.0 83.1 89.5 89.4 79.3 87.6 82.2 16.1 74.4 96.5 97.6
    Camera jitter 60.4 57.1 74.7 82.1 83.8 76.8 81.8 83.4 91.3 74.9 89.9 81.4 41.5 79.7 97.6 98.8
    Shadow 75.4 74.9 87.7 90.1 80.9 88.6 90.2 89.8 77.6 52.9 60.9 67.3 30.2 73.9 85.1 97.6
    Inter.ob.motion 58.1 59.2 62.6 75.3 74.7 77.1 72.8 73.4 87.1 83.9 89.9 84.6 75.2 86.9 94.1 95.6
    Thermal 68.6 67.7 78.5 83.4 82.1 81.3 82.4 83.9 83.3 63.4 75.8 83.1 40.9 79.6 90.7 90.6
    Bad weather 74.3 74.5 77.7 81.5 80.8 82.5 86.3 86.0 81.5 78.4 83.0 84.8 68.5 86.1 94.3 95.6
    Low frame-rate 63.1 59.1 62.6 75.6 63.6 70.0 75.3 75.1 65.8 61.0 60.1 72.9 46.4 66.0 83.7 85.7
    Night videos 46.3 45.0 47.8 53.8 52.0 54.4 58.9 60.3 41.5 49.8 58.4 54.2 44.6 59.3 89.6 90.6
    PTZ 20.1 64.5 29.7 45.6 56.7 40.1 42.3 45.1 46.1 23.5 31.3 38.6 13.8 38.4 91.6 93.6
    Air turbulence 55.6 48.2 66.9 78.8 61.2 72.9 79.3 79.8 64.5 69.3 84.6 73.4 15.2 75.4 91.8 91.7
    Average 61.4 64.1 68.0 77.4 73.6 75.1 76.9 78.3 74.7 65.4 74.2 74.3 41.2 73.9 92.1 93.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-09-30
  • 录用日期:  2018-02-26
  • 刊出日期:  2018-05-20

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