吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (4): 1286-1294.doi: 10.13229/j.cnki.jdxbgxb201704039

• 论文 • 上一篇    下一篇

视频监控的数据驱动背景模型

李志慧1, 夏英集1, 曲昭伟1, 任景琛2   

  1. 1.吉林大学 交通学院,长春 130022;
    2.北京师范大学 统计学院,北京 100875
  • 收稿日期:2016-04-28 出版日期:2017-07-20 发布日期:2017-07-20
  • 通讯作者: 曲昭伟(1962-),男,教授,博士生导师.研究方向:交通控制.E-mail:quzw@jlu.edu.cn
  • 作者简介:李志慧(1977-),男,副教授,博士.研究方向:交通视频检测与处理.E-mail:lizhih@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51278220); 吉林省重点科技攻关项目(20140204028SF); 吉林省科技发展计划重点项目(20130206093SF).

Data-driven background model in video surveillance

LI Zhi-hui1, XIA Ying-ji1, QU Zhao-wei1, REN Jing-chen2   

  1. 1.College of Transportation, Jilin University, Changchun 130022, China;
    2.School of Statistics, Beijing Normal University, Beijing 100875,China
  • Received:2016-04-28 Online:2017-07-20 Published:2017-07-20

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摘要： 针对现有背景模型假设引起的失效问题,根据数据驱动思想,建立了一种基于数据驱动的背景模型表示方法。该方法通过全格式动态线性化的无模型自适应控制方法,引入系统的伪梯度向量,结合多步历史数据,建立背景表达和选择性更新策略,获取视频背景。实验过程通过不同场景视频序列和经典背景模型方法进行对比,实验结果表明:本文算法具有背景更新效果较好、计算量适中、鲁棒性强等优点,且克服了机理模型中对模型假设的依赖及模型失效等问题。因此,本文基于数据驱动的背景模型算法可为在线视频检测系统的背景抽取提供有力的技术支持和参考。

关键词: 计算机应用, 背景更新, 无模型自适应控制, 背景差分, 视频监控

Abstract: The current modeling assumption may induce background distortion. To solve this problem, a new background model based on data-driven theory is proposed. Model-free adaptive control method is used to express the value of the online video sequence. The slow process of background illumination change is regarded as a nonlinear time-varying system, and is expressed via dynamic linearization using pseudo-gradient vector. Then, the expression is iterated with the combination of historical data and selective background update method to complete the background model iteration process. Experiments are carried out based on different video cases. Results show that the model can get better foreground and more stable background than Kalman filter and Gaussian-mixture model. Furthermore, the data-driven method of the proposed model overcomes the disadvantages of the mechanism models, and its computational efficiency and robustness make it applicable to online video processing and detection of moving objects.

Key words: computer application, background update, model-free adaptive control, background subtraction, video surveillance

中图分类号: 

  • U495
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