Segmentation of moving objects from video sequences is the fundamental step in intelligent surveillance applications. Numerous methods have been proposed to obtain object segmentation. In this paper, we present an effective approach based on the mixture of Gaussians. The approach makes use of a feedback strategy with multiple levels: the pixel level, the region level, and the frame level. Pixel-level feedback helps to provide each pixel with an adaptive learning rate. The maintenance strategy of the background model is adjusted by region-level feedback based on tracking. Frame-level feedback is used to detect the global change in scenes. These different levels of feedback strategies ensure our approach's effectiveness and robustness. This is demonstrated through experimental results on the Change Detection 2014 benchmark dataset.
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