Journal of South China University of Technology(Natural Science Edition) ›› 2012, Vol. 40 ›› Issue (9): 116-122.

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Combinational Gaussian Background Modeling Method Based on Analysis of Spatio-Temporal Entropy

Song Jia-sheng  Hu Guo-qing   

  1. School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2011-09-23 Revised:2012-06-25 Online:2012-09-25 Published:2012-08-01
  • Contact: 宋佳声(1976-) ,男,在职博士生,集美大学讲师,主要从事图像处理与模式识别研究. E-mail:songjs@ gmail.com
  • About author:宋佳声(1976-) ,男,在职博士生,集美大学讲师,主要从事图像处理与模式识别研究.
  • Supported by:

    国家自然科学基金资助项目( 50875177)

Abstract:

In order to increase the update speed of Gaussian models for the background in video surveillance systems,the concept and computation method of the scene moving complexity are devised,according to which a combinational Gaussian background modeling method is devised. In this method, according to the spatio-temporal model of pixels,the scene moving complexity is analyzed and the entropy image of the scene is calculated. Then,this image is segmented into the stable region and the dynamic region by means of the maximum entropy threshold. In the two different regions,two different Gaussian models and corresponding updating algorithms are respectively adopted. Finally,the devised method is used to implement the foreground segmentation of the video sequences with a size of 384 × 288 pixels. The results show that the devised method is of a greater update speed for the background model and can effectively segment moving objects.

Key words: background modeling, spatio-temporal entropy, Gaussian distribution, combinational Gaussian model, foreground segmentation

CLC Number: