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基于误差补偿的复杂场景下背景建模方法

秦明 陆耀 邸慧军 吕峰

秦明, 陆耀, 邸慧军, 吕峰. 基于误差补偿的复杂场景下背景建模方法. 自动化学报, 2016, 42(9): 1356-1366. doi: 10.16383/j.aas.2016.c150857
引用本文: 秦明, 陆耀, 邸慧军, 吕峰. 基于误差补偿的复杂场景下背景建模方法. 自动化学报, 2016, 42(9): 1356-1366. doi: 10.16383/j.aas.2016.c150857
QIN Ming, LU Yao, DI Hui-Jun, LV Feng. An Error Compensation Based Background Modeling Method for Complex Scenarios. ACTA AUTOMATICA SINICA, 2016, 42(9): 1356-1366. doi: 10.16383/j.aas.2016.c150857
Citation: QIN Ming, LU Yao, DI Hui-Jun, LV Feng. An Error Compensation Based Background Modeling Method for Complex Scenarios. ACTA AUTOMATICA SINICA, 2016, 42(9): 1356-1366. doi: 10.16383/j.aas.2016.c150857

基于误差补偿的复杂场景下背景建模方法

doi: 10.16383/j.aas.2016.c150857
基金项目: 

国家自然科学基金 61175096

国家自然科学基金 61271374

高等学校博士学科点专项科研基金 2012110110034

国家自然科学基金 61273273

详细信息
    作者简介:

    秦明北京理工大学计算机学院博士研究生.主要研究方向为前景检测与动作识别.E-mail:050689@bit.edu.cn

    邸慧军北京理工大学计算机学院讲师.主要研究方向为计算机视觉, 模式识别, 机器学习.E-mail:ajon@bit.edu.cn

    吕峰北京理工大学计算机学院博士研究生.主要研究方向为目标跟踪与动作识别.E-mail:lvfeng@bit.edu.cn

    通讯作者:

    陆耀北京理工大学计算机学院教授.主要研究方向为神经网络, 图像和信号处理, 模式识别.本文通信作者.E-mail:vis_yl@bit.edu.cn

An Error Compensation Based Background Modeling Method for Complex Scenarios

Funds: 

National Natural Science Foundation of China 61175096

National Natural Science Foundation of China 61271374

Research Fund for the Doctoral Program of Higher Education of China 2012110110034

National Natural Science Foundation of China 61273273

More Information
    Author Bio:

    Ph. D. candidate at the School of Computer Science, Beijing Institute of Technology. His research interest covers foreground detection and action recognition

    Lecturer at the School of Computer Science, Beijing Institute of Technology. His research interest covers computer vision, pattern recognition and machine learning

    Ph. D. candidate at the School of Computer Science, Beijing Institute of Technology. His research interest covers object tracking and action recognition

    Corresponding author: Professor at the School of Computer Science, Beijing Institute of Technology. His research interest covers neural network, image and signal processing and pattern recognition. Corresponding author of this paper
  • 摘要: 在基于子空间学习的背景建模方法中,利用背景信息对前景误差进行补偿有助于建立准确的背景模型.然而,当动态背景(摇曳的树枝、波动的水面等)和复杂前景等干扰因素存在时,补偿过程的准确性和稳定性会受到一定的影响.针对这些问题,本文提出了一种基于误差补偿的增量子空间背景建模方法.该方法可以实现复杂场景下的背景建模.首先,本文在误差补偿的过程中考虑了前景的空间连续性约束,在补偿前景信息的同时减少了动态背景的干扰,提高了背景建模的准确性.其次,本文将误差估计过程归结为一个凸优化问题,并根据不同的应用场合设计了相应的精确求解算法和快速求解方法.再次,本文设计了一种基于Alpha通道的误差补偿策略,提高了算法对复杂前景的抗干扰能力.最后,本文构建了不依赖于子空间模型的背景模板,减少了由前景信息反馈引起的背景更新失效,提高了算法的鲁棒性.多项对比实验表明,本文算法在干扰因素存在的情况下仍然可以实现对背景的准确建模,表现出较强的抗扰性和鲁棒性.
  • 图  1  基于抗干扰误差补偿的背景建模算法流程示意图

    Fig.  1  The flow chart of the proposed robust error compensation based background modeling method

    图  2  前景的空间连续性约束

    Fig.  2  The spatial continuity constraint on foreground

    图  3  前景信息的正反馈问题

    Fig.  3  The positive feedback of foreground information

    图  4  二值分类函数与保留距离信息的分类策略

    Fig.  4  Comparison between binary classification function and distance information preservation based classification strategy

    图  5  前景信息的正反馈回路

    Fig.  5  The positive feedback loop of foreground information

    图  6  背景模板更新间隔p对算法平均性能的影响

    Fig.  6  The average F-score performance with respect to different parameter p

    图  7  本文算法的三个主要组成部分的有效性展示

    Fig.  7  The effectiveness of the main three components in the proposed algorithm

    图  8  不同前景检测算法的前景掩膜结果

    Fig.  8  The foreground masks obtained from different foreground detection algorithms

    表  1  本文算法与其他算法的F-score得分(%)

    Table  1  The F-score results (%) of the proposed method and the other methods

    DPGMM RPCA GoDec GRASTA LRFSO RFDSA DECOLOR Ours(fast) Ours(accurate)
    Bootstrap 60.24 61.19 60.91 59.45 56.68 68.41 69.74 62.54 69.68
    Campus 75.67 29.19 24.26 18.38 36.08 67.79 74.59 42.59 69.47
    Curtain 82.03 54.97 63.03 78.91 79.35 89.76 87.35 87.00 90.34
    Escalator 50.55 56.77 47.33 38.24 48.01 63.53 75.00 49.18 76.61
    Fountain 70.49 68.81 70.31 63.86 70.58 75.44 87.87 78.74 80.15
    ShoppingMall 65.22 70.03 66.53 68.96 54.46 74.07 65.58 71.78 75.40
    WaterSurface 90.90 48.48 66.56 78.34 76.69 87.96 54.27 89.21 89.43
    Hall 54.84 51.75 51.15 53.49 49.08 66.73 55.88 68.16 68.85
    Average 68.74 55.14 56.26 57.45 58.8 74.21 71.29 68.65 77.50
    FPS N/A 9.97 20.28 62.90 0.02 2.66 1.88 35.34 3.12
    下载: 导出CSV
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  • 收稿日期:  2015-12-18
  • 录用日期:  2016-04-20
  • 刊出日期:  2016-09-01

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