The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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Adaptive Gaussian Mixture Learning for High Traffic Region

혼잡한 환경에서 적응적 가우시안 혼합 모델을 이용한 배경의 학습 및 객체 검출

  • 박대용 (홍익대학 전기정보제어공학과) ;
  • 김재민 (홍익대학 전기정보제어공학과) ;
  • 조성원 (홍익대학 전기정보제어공학과)
  • Published : 2006.02.01
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Abstract

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.

Keywords

References

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