  We present an approach for object segmentation in videos that combines
frame-level object detection with concepts from object tracking and motion
segmentation. The approach extracts temporally consistent object tubes based on
an off-the-shelf detector. Besides the class label for each tube, this provides
a location prior that is independent of motion. For the final video
segmentation, we combine this information with motion cues. The method
overcomes the typical problems of weakly supervised/unsupervised video
segmentation, such as scenes with no motion, dominant camera motion, and
objects that move as a unit. In contrast to most tracking methods, it provides
an accurate, temporally consistent segmentation of each object. We report
results on four video segmentation datasets: YouTube Objects, SegTrackv2,
egoMotion, and FBMS.
