Unsupervised video segmentation plays an important role in a wide variety of
applications from object identification to compression. However, to date, fast
motion, motion blur and occlusions pose significant challenges. To address
these challenges for unsupervised video segmentation, we develop a novel
saliency estimation technique as well as a novel neighborhood graph, based on
optical flow and edge cues. Our approach leads to significantly better initial
foreground-background estimates and their robust as well as accurate diffusion
across time. We evaluate our proposed algorithm on the challenging DAVIS,
SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge
detector trained on 200 images, our method achieves state-of-the-art results
outperforming deep learning based methods in the unsupervised setting. We even
demonstrate competitive results comparable to deep learning based methods in
the semi-supervised setting on the DAVIS dataset.

本文提出了基于光流和边缘线索的新型显着性估计技术和新型邻域图，用于解决快速运动，运动模糊和遮挡等视频分割的挑战，在 DAVIS，SegTrack v2 和 FBMS-59 数据集中取得了超越深度学习方法的最新成果。