We propose an effective online background subtraction method, which can be
robustly applied to practical videos that have variations in both foreground
and background. Different from previous methods which often model the
foreground as Gaussian or Laplacian distributions, we model the foreground for
each frame with a specific mixture of Gaussians (MoG) distribution, which is
updated online frame by frame. Particularly, our MoG model in each frame is
regularized by the learned foreground/background knowledge in previous frames.
This makes our online MoG model highly robust, stable and adaptive to practical
foreground and background variations. The proposed model can be formulated as a
concise probabilistic MAP model, which can be readily solved by EM algorithm.
We further embed an affine transformation operator into the proposed model,
which can be automatically adjusted to fit a wide range of video background
transformations and make the method more robust to camera movements. With using
the sub-sampling technique, the proposed method can be accelerated to execute
more than 250 frames per second on average, meeting the requirement of
real-time background subtraction for practical video processing tasks. The
superiority of the proposed method is substantiated by extensive experiments
implemented on synthetic and real videos, as compared with state-of-the-art
online and offline background subtraction methods.

本文提出了一种基于高斯混合模型 (MoG) 的在线背景消除方法，该方法使用了一种特殊的 MoG 混合模型对每一帧的前景进行建模，并通过前一帧的前景 / 背景知识对 MoG 混合模型进行规范化。此外，本文还嵌入了仿射变换操作符来适应各种视频背景变换，并通过子采样技术来实现实时视频处理。与现有的在线和离线背景消除方法相比，实验表明，所提出的方法具有优越性能。