本文提出了一种高效算法，用于解决非约束条件视频中前景/背景分割的问题。该算法通过在视频序列中相似（反复出现）区域的图上设置投票方案来实现，能够处理高度非刚性运动、复杂摄像机运动、模糊运动、大规模光照变化等多种情况。通过迭代纠正初值并利用区域在空间和时间上的非局部性进行共识投票，算法能够在各种非约束视频上产生准确的结果。
Video Segmentation by Non-Local Consensus Voting
Alon Faktor and Michal Irani(BMVC 2014)
Abstract
We address the problem of Foreground/Background segmentation of "unconstrained" video. By "unconstrained" we mean that the moving objects and the background scene may be highly non-rigid (e.g., waves in the sea); the camera may undergo a complex motion with 3D parallax; moving objects may suffer from motion blur, large scale and illumination changes, etc. Most existing segmentation methods fail on such unconstrained videos, especially in the presence of highly non-rigid motion and low resolution. We propose a computationally efficient algorithm which is able to produce accurate results on a large variety of unconstrained videos. This is obtained by casting the video segmentation problem as a voting scheme on the graph of similar ('re-occurring') regions in the video sequence. We start from crude saliency votes at each pixel, and iteratively correct those votes by 'consensus voting' of re-occurring regions across the video sequence. The power of our consensus voting comes from the non-locality of the region re-occurrence, both in space and in time - enabling propagation of diverse and rich information across the entire video sequence. Qualitative and quantitative experiments indicate that our approach outperforms current state-of-the-art methods.
Code can be downloaded here
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