The human ability to detect and segment moving objects works in the presence
of multiple objects, complex background geometry, motion of the observer, and
even camouflage. In addition to all of this, the ability to detect motion is
nearly instantaneous. While there has been much recent progress in motion
segmentation, it still appears we are far from human capabilities. In this
work, we derive from first principles a new likelihood function for assessing
the probability of an optical flow vector given the 3D motion direction of an
object. This likelihood uses a novel combination of the angle and magnitude of
the optical flow to maximize the information about the true motions of objects.
Using this new likelihood and several innovations in initialization, we develop
a motion segmentation algorithm that beats current state-of-the-art methods by
a large margin. We compare to five state-of-the-art methods on two established
benchmarks, and a third new data set of camouflaged animals, which we introduce
to push motion segmentation to the next level.

本研究基于第一原理推导出新的似然函数，使用角度和光流大小的新组合来最大化关于物体真实运动的信息，开发了一种运动分割算法，比当前最先进的方法提高了很大的准确率，并在两个已建立的基准测试和一个新的伪装动物数据集上进行了对比。