Multi-stage Optimization for Multi-body Motion Segmentation

Many techniques have been proposed for separating feature point trajectories tracked through a video sequence into independent motions, but objects are usually assumed to undergo general 3-D motions. As a result, the separation accuracy considerably deteriorates in realistic video sequences in which object motions are nearly degenerate. In this paper, we introduce unsupervised learning assuming degenerate motions followed by unsupervised learning assuming general 3-D motions. This multi-stage optimization allows us to not only separate simple motions that we frequently encounter with high precision but also preserve the high performance for considerably general 3-D motions. Doing simulations and real video experiments, we demonstrate that our method is superior to all existing methods.

针对将视频序列中跟踪到的特征点轨迹分离为独立运动，人们已经提出了许多技术，但通常假定物体进行一般的三维运动。结果，在物体运动近乎退化的实际视频序列中，分离精度会大幅下降。在本文中，我们引入了假定退化运动的无监督学习，然后是假定一般三维运动的无监督学习。这种多阶段优化使我们不仅能够高精度地分离我们经常遇到的简单运动，而且对于相当一般的三维运动也能保持高性能。通过模拟和真实视频实验，我们证明我们的方法优于所有现有方法。