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基于长时间视频序列的背景建模方法研究

丁洁 肖江剑 况立群 宋康康 彭成斌

丁洁, 肖江剑, 况立群, 宋康康, 彭成斌. 基于长时间视频序列的背景建模方法研究. 自动化学报, 2018, 44(4): 707-718. doi: 10.16383/j.aas.2017.c160468
引用本文: 丁洁, 肖江剑, 况立群, 宋康康, 彭成斌. 基于长时间视频序列的背景建模方法研究. 自动化学报, 2018, 44(4): 707-718. doi: 10.16383/j.aas.2017.c160468
DING Jie, XIAO Jiang-Jian, KUANG Li-Qun, SONG Kang-Kang, PENG Cheng-Bin. Background Modeling for Long-term Video Sequences. ACTA AUTOMATICA SINICA, 2018, 44(4): 707-718. doi: 10.16383/j.aas.2017.c160468
Citation: DING Jie, XIAO Jiang-Jian, KUANG Li-Qun, SONG Kang-Kang, PENG Cheng-Bin. Background Modeling for Long-term Video Sequences. ACTA AUTOMATICA SINICA, 2018, 44(4): 707-718. doi: 10.16383/j.aas.2017.c160468

基于长时间视频序列的背景建模方法研究

doi: 10.16383/j.aas.2017.c160468
基金项目: 

浙江省杰出青年基金 LR13F020004

钱江人才计划 QJD1702031

国家科技支撑计划 2015BAF14B01

中国博士后科学基金 2017M612047

国家自然科学基金 61273276

国家自然科学基金 61379080

详细信息
    作者简介:

    丁洁, 中北大学计算机与控制工程学院硕士研究生.主要研究方向为计算机视觉, 虚拟仿真与可视化.E-mail:jie_ding@163.com

    况立群, 中北大学计算机与控制工程学院副教授.主要研究方向为仿真与可视化, 图像处理, 虚拟现实.E-mail:liqun_kuang@163.com

    宋康康, 中国科学院宁波工业技术研究院工程师.主要研究方向为图像处理, 计算机视觉.E-mail:songkk@nimte.ac.cn

    彭成斌, 中国科学院宁波工业技术研究院副研究员.主要研究方向为数据挖掘, 模式识别和并行计算.E-mail:pengchengbin@nimte.ac.cn

    通讯作者:

    肖江剑, 中国科学院宁波工业技术研究院研究员.主要研究方向为计算机视觉, 图像和视频处理, 车辆跟踪与智能交通, 模式识别.本文通信作者.E-mail:xiaojj@nimte.ac.cn

Background Modeling for Long-term Video Sequences

Funds: 

Excellent Youth Foundation of Zhejiang Scientific Committee LR13F020004

Qianjiang Talent Program QJD1702031

National Key Technology R&D Program 2015BAF14B01

China Postdoctoral Science Foundation 2017M612047

Supported by National Natural Science Foundation of China 61273276

Supported by National Natural Science Foundation of China 61379080

More Information
    Author Bio:

    Master student at the School of Computer and Control Engineering, North University of China. Her research interest covers computer vision, virtual simulation and visualization

    Associate professor at the School of Computer and Control Engineering, North University of China. His research interest covers virtual simulation and visualization, image processing and virtual reality

    Engineer at Ningbo Institute of Industrial Technology, Chinese Academy of Sciences. His research interest covers computer vision, image processing

    Associate researcher at Ningbo Institute of Industrial Technology, Chinese Academy of Sciences. His research interest covers data mining, pattern recognition, and parallel computing

    Corresponding author: XIAO Jiang-Jian Research fellow at Ningbo Institute of Industrial Technology, Chinese Academy of Sciences. His research interest covers computer vision, image and video processing, vehicle tracking and intelligent transportation and pattern recognition. Corresponding author of this paper
  • 摘要: 针对现有背景建模算法难以处理场景非平稳变化的问题,提出一种基于长时间视频序列的背景建模方法.该方法包括训练、检索、更新三个主要步骤.在训练部分,首先将长时间视频分段剪辑并计算对应的背景图,然后通过图像降采样和降维找到背景描述子,并利用聚类算法对背景描述子进行分类,生成背景记忆字典.在检索部分,利用前景像素比例设计非平稳状态判断机制,如果发生非平稳变换,则计算原图描述子与背景字典中描述子之间的距离,距离最近的背景描述子对应的背景图片即为此时背景.在更新部分,利用前景像素比例设计更新判断机制,如果前景比例始终过大,则生成新背景,并更新背景字典以及背景图库.当出现非平稳变化时(如光线突变),本算法能够将背景模型恢复问题转化为背景检索问题,确保背景模型的稳定获得.将该框架与短时空域信息背景模型(以ViBe、MOG为例)融合,重点测试非平稳变化场景下的背景估计和运动目标检测结果.在多个视频序列上的测试结果表明,该框架可有效处理非平稳变化,有效改善目标检测效果,显著降低误检率.
    1)  本文责任编委 桑农
  • 图  1  长视频背景建模框架

    Fig.  1  Long time background modeling framework

    图  2  随机决策树

    Fig.  2  Random decision tree

    图  3  背景字典生成图

    Fig.  3  Map of background dictionary

    图  4  贡献率图

    Fig.  4  Contribution rate

    图  5  谱聚类中拉普拉斯矩阵特征值图

    Fig.  5  Laplacian eigenvalues graph of spectral clustering

    图  6  不同维数的聚类效果

    Fig.  6  Cluster results of different dimension

    图  7  不同聚类个数效果图(32维)

    Fig.  7  Cluster results of different cluster number (32)

    图  8  光线突变阈值$T$的确定

    Fig.  8  Determination of sudden illumination change threshold $T$

    图  9  阈值$T$的逻辑回归分析

    Fig.  9  Logistic regression analysis of threshold $T$

    图  10  更新背景字典阈值$T_{u}$的确定

    Fig.  10  Determination of threshold $T_{u}$ for updating background dictionary

    图  12  运动目标检测效果对比图(ViBe开灯)

    Fig.  12  Moving object detection comparison charts (ViBe turns on the lights

    图  14  前景像素比例变化对比图(对应图 12 (c) $\sim$(d))

    Fig.  14  Comparison chart of foreground pixel ratio (Corresponding to Fig. 12 (c) $\sim$ (d))

    图  11  运动目标检测效果对比图(ViBe关灯)

    Fig.  11  Moving object detection comparison charts (ViBe turns off the lights)

    图  13  前景像素比例变化对比图(对应图 11 (a) $\sim$ (b))

    Fig.  13  Comparison chart of foreground pixel ratio (Corresponding to Fig. 11 (a) $\sim$ (b))

    图  15  运动目标检测效果对比图(MOG关灯)

    Fig.  15  Moving object detection comparison charts (MOG turns off the lights)

    图  16  运动目标检测效果对比图(MOG开灯)

    Fig.  16  Moving object detection comparison charts (MOG turns on the lights)

    图  17  室外情况运动目标检测情况

    Fig.  17  Moving object detection of outdoor

    表  1  算法处理速度(fps)

    Table  1  Processing times of algorithm (fps)

    算法 Data1 Data2 Data3 Data4
    原ViBe算法 25.96 63.79 62.44 14.49
    本文算法 25.65 63.13 59.48 14.40
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-06-15
  • 录用日期:  2016-11-23
  • 刊出日期:  2018-04-20

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