Fast Object Segmentation in Unconstrained Video

We present a technique for separating foreground objects from the background in a video. Our method is fast, fully automatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object motion and appearance, and non-rigid deformations and articulations. In experiments on two datasets containing over 1400 video shots, our method outperforms a state-of-the-art background subtraction technique 4 as well as methods based on clustering point tracks 6, 18, 19. Moreover, it performs comparably to recent video object segmentation methods based on object proposals 14, 16, 27, while being orders of magnitude faster.