Modeling and copying human head movements

Abstract

Derives two discrete motion models for three-dimensional (3-D) pose recovery starting from the stochastic differential equations that describe the object's motion in continuous time. The velocity is considered first as a Wiener process, which underlies the very often used constant velocity model, and second as an Ornstein-Uhlenbeck process. Analysis of the autocorrelation signal derived in experiment from visual tracking of both translational and rotational movements of a human head demonstrates that such muscular motion is better suited to modeling by the Ornstein-Uhlenbeck velocity process. The model is embedded in an iterated extended Kalman filter, which linearizes about the predicted pose and in which care is taken properly to transform the state covariance, and incorporated in a system able visually to recover and track the pose of a human operator's head. The pose is copied onto a slave electromechanical stereo camera platform alone, providing two rotational degrees of freedom, or onto the same head carried by a robot arm to give complete six degree-of-freedom mapping of the operator's movements. Accuracy and frequency response are assessed.