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

• Orginal Article • Previous Articles     Next Articles

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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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

CLC Number: 

  • U495
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