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.