Poster: Real time hand pose recognition with depth sensors for mixed reality interfaces

We present a method for predicting articulated hand poses in real-time with a single depth camera, such as the Kinect or Xtion Pro, for the purpose of interaction in a Mixed Reality environment and for studying the effects of realistic and non-realistic articulated hand models in a Mixed Reality simulator. We demonstrate that employing a randomized decision forest for hand recognition benefits real-time applications without the typical tracking pitfalls such as reinitialization. This object recognition approach to predict hand poses results in relatively low computation, high prediction accuracy and sets the groundwork needed to utilize articulated hand movements for 3D tasks in Mixed Reality workspaces.