A General Inlier Estimation for Moving Camera Motion Segmentation

In moving camera videos, motion segmentation is often achieved by determining the motion coherence of each moving object. However, it is a nontrivial task on optical flow due to two problems: 1) Optical flow of the camera motions in 3D world consists of three primary 2D motion flows: translation, rotation, and radial flow. Their coherence analysis is done by a variety of models, and further requires plenty of priors in existing frameworks; 2) A moving camera introduces 3D motion, the depth discontinuities cause the motion discontinuities that severely break down the coherence. Meanwhile, the mixture of the camera motion and moving objects’ motions make it difficult to clearly identify foreground and background. In this work, our solution is to transform the optical flow into a potential space where the coherence of the background flow field is easily modeled by a low order polynomial. To this end, we first amend the Helmholts-Hodge Decomposition by adding coherence constraints, which can transform translation, rotation, and radial flow fields to two potential surfaces under a unified framework. Secondly, we introduce an Incoherence Map and a progressive Quad-Tree partition to reject moving objects and motion discontinuities. Finally, the low order polynomial is achieved from the rest flow samples on two potentials. We present results on more than twenty videos from four benchmarks. Extensive experiments demonstrate better performance in dealing with challenging scenes with complex backgrounds. Our method improves the segmentation accuracy of state-of-the-arts by 10%∼30%.