A novel algorithm of adaptive background estimation

Author

Gao, Da-shan ; Zhou, Jie ; Xin, Le-ping

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

2

fYear

2001

Firstpage

395

Abstract

We propose an adaptive background estimation algorithm for an outdoor video surveillance system. In order to enhance the ability of adaptation to illumination changes and variant noise in long-term running, an improved Kalman filtering model based on the local-region is discussed to dynamically estimate a background image, in which the parameters are predicted by a recursive-least-square adaptive filter. The experimental results on real-world video show that the algorithm can perform robustly and effectively

Keywords

Kalman filters; adaptive estimation; adaptive filters; image enhancement; least squares approximations; recursive estimation; recursive filters; statistical analysis; surveillance; video signal processing; Kalman filtering model; RLS filter; adaptive background estimation algorithm; adaptive filter; background image; dynamic estimation; illumination changes; image enhancement; image histogram; local region; outdoor video surveillance system; parameter prediction; real-time traffic monitoring; recursive-least-square filter; variant noise; Adaptive filters; Background noise; Change detection algorithms; Filtering; Kalman filters; Lighting; Pixel; Recursive estimation; Vehicle detection; Video surveillance;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958511

Filename

958511