Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Layered Object Detection using Adaptive Gaussian Mixture Model in the Complex and Dynamic Environment

혼잡한 환경에서 적응적 가우시안 혼합 모델을 이용한 계층적 객체 검출

  • 이진형 (홍익대학교 전기정보제어공학과) ;
  • 조성원 (홍익대학교 전기정보제어공학과) ;
  • 김재민 (홍익대학교 전기정보제어공학과) ;
  • 정선태 (숭실대학교 정보통신전자공학부)
  • Published : 2008.06.25
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Abstract

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

움직이는 객체를 검출하기 위해서 정확한 배경을 사용하기 위해 널리 사용되는 방법으로는 가우시안 혼합 모델이다. 가우시안 혼합 모델은 확률적 학습 방법을 사용하는데, 이 방법은 움직이는 배경일 경우와 이동하던 물체가 정지하는 경우 배경을 정확히 모델링하지 못한다. 본 논문에서는 확률적 모델링을 통해 혼잡한 배경을 모델링하고 객체의 계층적 처리를 통해 보다 정확한 배경으로 갱신할 수 있는 학습 방법을 제안한다.

Keywords

References

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Cited by

  1. Object Segmentation/Detection through learned Background Model and Segmented Object Tracking Method using Particle Filter vol.20, pp.8, 2016, https://doi.org/10.6109/jkiice.2016.20.8.1537