| Title |
Adaptive Gaussian Mixture Learning for High Traffic Region |
| Authors |
박대용(Park Dae-Yong) ; 김재민(Kim Jae-Min) ; 조성원(Cho Seong-Won) |
| Keywords |
GMM ; Background Modeling ; Motion Detection ; Background Subtraction ; High Traffic Region |
| 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. |