Segmentation of Moving Objects by Long Term Video Analysis

Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed to classical two-frame optical flow, point trajectories that span hundreds of frames are less susceptible to short-term variations that hinder separating different objects. As a positive side effect, the resulting groupings are temporally consistent over a whole video shot, a property that requires tedious post-processing in the vast majority of existing approaches. We suggest working with a paradigm that starts with semi-dense motion cues first and that fills up textureless areas afterwards based on color. This paper…

• MR
  Microsoft Research
• JH
  Johns Hopkins University
• AF
  Albert-Ludwigs-Universität Freiburg
• UD
  Universität des Saarlandes