The exponentially increasing use of moving platforms for video capture
introduces the urgent need to develop the general background subtraction
algorithms with the capability to deal with the moving background. In this
paper, we propose a multilayer-based framework for online background
subtraction for videos captured by moving cameras. Unlike the previous
treatments of the problem, the proposed method is not restricted to binary
segmentation of background and foreground, but formulates it as a multi-label
segmentation problem by modeling multiple foreground objects in different
layers when they appear simultaneously in the scene. We assign an independent
processing layer to each foreground object, as well as the background, where
both motion and appearance models are estimated, and a probability map is
inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut
on Markov Random Field is employed to perform pixel-wise labeling. Extensive
evaluation results show that the proposed method outperforms state-of-the-art
methods on challenging video sequences.

针对移动拍摄视频中背景难以识别的问题，本文提出了一种基于多层结构的在线背景减除算法框架，将多前景物体建模并赋予拥有独立处理层，同时采用贝叶斯滤波框架推断概率图，并用马尔科夫随机场上的多标签图割算法进行像素级分割，取得了优于现有技术的结果。