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

초록

- 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.

키워드