In moving camera videos, motion segmentation is commonly performed using the
image plane motion of pixels, or optical flow. However, objects that are at
different depths from the camera can exhibit different optical flows even if
they share the same real-world motion. This can cause a depth-dependent
segmentation of the scene. Our goal is to develop a segmentation algorithm that
clusters pixels that have similar real-world motion irrespective of their depth
in the scene. Our solution uses optical flow orientations instead of the
complete vectors and exploits the well-known property that under camera
translation, optical flow orientations are independent of object depth. We
introduce a probabilistic model that automatically estimates the number of
observed independent motions and results in a labeling that is consistent with
real-world motion in the scene. The result of our system is that static objects
are correctly identified as one segment, even if they are at different depths.
Color features and information from previous frames in the video sequence are
used to correct occasional errors due to the orientation-based segmentation. We
present results on more than thirty videos from different benchmarks. The
system is particularly robust on complex background scenes containing objects
at significantly different depths

本文提出了一种运动分割算法，通过使用光流方向代替完整的光流向量，以及使用概率模型自动估计观测到的独立运动数量，实现了对具有相似世界实际运动的像素进行聚类，从而有效避免了景深不同导致的深度相关分割。该系统在多个基准测试视频中表现出鲁棒性，特别是对包含具有相差很大深度的物体的复杂背景场景的运动分割。