Adaptive Background Modeling for Crowded Scenes

혼잡한 환경에 적합한 적응적인 배경모델링 방법

  • 이광국 (한양대학교 전자컴퓨터통신공학과) ;
  • 송수한 (삼성전자 디지털미디어총괄 디지털프린팅사업부) ;
  • 가기환 (한양대학교 전자컴퓨터통신공학과) ;
  • 윤자영 (한양대학교 건축환경공학과) ;
  • 김재준 (한양대학교 건축공학부) ;
  • 김회율 (한양대학교 전자통신컴퓨터공학부)
  • Received : 2008.01.25
  • Accepted : 2008.03.25
  • Published : 2008.05.31


Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.


Supported by : 한국건설교통기술평가원