title: Linearized motion estimation for articulated planes.
abstract: In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations 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 this, 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 enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of 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.
authors.affiliation: Carnegie Mellon University, Pittsburgh, USA. ankurd@cs.cmu.edu
authors.forename: Ankur
authors.initials: A
authors.lastname: Datta
authors.forename: Yaser
authors.initials: Y
authors.lastname: Sheikh
authors.forename: Takeo
authors.initials: T
authors.lastname: Kanade
mesh_terms: D000465:Algorithms; D006225:Hand / Q000033:anatomy & histology / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D009038:Motion; D009068:Movement