Range Image Sequence Analysis by 2.5-d Least Squares Tracking with Variance Component Estimation and Robust Variance Covariance Matrix Estimation

In this article, a range image sequence tracking approach is proposed, which combines 3-D camera intensity and range observations in an integrated geometric transformation model. Based on 2-D least squares matching, a closed solution for intensity and range observations has been developed. By combining complementary information, an increase in accuracy and reliability can be achieved. The weighting of the two different types of observations with a-priori unknown quality is performed by variance component estimation. To fulfill the requirements of robust variance covariance matrix estimation in statistical context, alternative approaches for variance covariance matrix calculation are proposed and evaluated. To verify its applicability, reliability and accuracy potential, the introduced 2.5-D least squares tracking technique has been evaluated by several series of experiments in the field of human motion and interaction measurement.