Background Modeling Approach Based on Self-adaptive Learning Rate
Li Wei, Linqiang Chen, Yin Weiliang · Jisuanji gongcheng · 2011
This paper proposes a background modeling approach based on self-adaptive learning rat aiming at the update of the learning rate about Gaussian mixture model.The initial background is established using the traditional Gaussian mixture model with a global learning rate.The self-adaptive learning rate is used for each pixel according to the number of matching when the background is updated.Experimental results show that compared with moving object detection approach based on conventional Gaussian mixture model,it has a desirable stability and learning ability.