Model-based human pose estimation with hierarchical ICP from single depth images

Abstract

In this paper, human poses which are presented by real 3D points cloud got from Kinect sensor are estimated and tracked by a hierarchical human model in ICP framework. There are several novel points in this paper. First, we compute human models' nearest points rather than points' nearest limbs as traditional methods do to make every limb have points. Second, we consider global information while hierarchically do ICP for every local limbs to conserve articulated kinematics chain. Third, by analyzing the four limbs (two legs and two arms) and enforcing joint constraints, we solve several specific problems, such as leg- or arm-crossing, etc. Experimental results including kinds of real human actions verify our method's effectiveness. © 2011 Springer-Verlag.