Linearized Motion Estimation for Articulated Planes

Abstract

Abstract—In this paper, we describe the explicit application of ar ticulation constraints for estimating the motion of a system of articulated planes. We relate ar ticulations to the relative homography between planes and show that these ar ticulations translate into linearized equality constraints on a linear least squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforce ar ticulation constraints. We show that explicit application of ar ticulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes and motion estimation of triangulated meshes.