Real-time foreground segmentation based on a fused background model
A fused background model that combines the eigenbackground with Gaussian models is proposed. We adopt the eigenspace model to build the intensity information for each pixel. Unimodal Gaussian density methods with less computational cost are used to describe color information for each pixel. An adaptive strategy is used to integrate the two models. Using the fused background model, we subtract the background from the current video frame to obtain the foreground object. Shadow removal based on chroma color method and post-processing are discussed in the end. Experimental results prove that our model is robust to noise and illumination change due to inheriting eigenbackground and Gaussian's model advantages to improve the segmentation results.