  We segment moving objects in videos by ranking spatio-temporal segment
proposals according to "moving objectness": how likely they are to contain a
moving object. In each video frame, we compute segment proposals using multiple
figure-ground segmentations on per frame motion boundaries. We rank them with a
Moving Objectness Detector trained on image and motion fields to detect moving
objects and discard over/under segmentations or background parts of the scene.
We extend the top ranked segments into spatio-temporal tubes using random
walkers on motion affinities of dense point trajectories. Our final tube
ranking consistently outperforms previous segmentation methods in the two
largest video segmentation benchmarks currently available, for any number of
proposals. Further, our per frame moving object proposals increase the
detection rate up to 7\% over previous state-of-the-art static proposal
methods.
