3D Pose Estimation of a Front-Pointing Hand Using a Random Regression Forest

In this paper, we propose a method for estimating the 3D poses of a front-pointing hand from camera images to realize freehand pointing interaction from a distance. Our method uses a Random Regression Forest (RRF) to realize robust estimation against environmental and individual variations. In order to improve the estimation accuracy, our method supports the use of two cameras and integrates the distributions of the hand poses for these cameras, which are modeled by the Gaussian mixture model. Moreover, tracking of the hand poses further improves the estimation accuracy and stability. The results of performance evaluation showed that the root mean square error of the angle estimation was 4.10., which is accurate enough to expect that our proposed method can be applied to user interface systems.