An Improved Gaussian Mixture Background Model with Real-time Adjustment of Learning Rate
In this paper, an adaptive background modeling approach for moving object detection is proposed. Based on mixture Gaussian model suggested by Stauffer, a mixture Gaussians model has been built for each pixel and its learning rate can be adjusted dynamically according to the scene change from the frame difference. This approach has changed the strategy used in various improvements to reinitialize the model on the condition of light suddenly change. Experiments show that the adaptive background model proposed in this paper has good adaptability to complex environments, the convergence rate of the model can be speeded up, and the moving object can be detected effectively and rapidly in the case of light suddenly changing.
Gaussian mixture model background modeling frame difference object detection learning rate
Li Ying-hong Tian Hong-fang Zhang Yan
Laboratory of Intelligent Transportation System North China University of Technology Beijing,China
国际会议
昆明
英文
512-515
2010-10-17（万方平台首次上网日期，不代表论文的发表时间）