Abstract
In this thesis, we propose a real-time system to estimate hand pose from depth image captured by Kinect. Different from model-based or appearance-based method, our system generates continuous output in a short period of time. The system consists of two main stages: finger joint locating and joint angle computation. First, we segment the hand region from depth image, and extract some specific hand feature points by using random forest classifier. Then, we use the relative displacement of those feature points to build the new feature vector, and estimate the joint angle by regression forest. Based on the estimation results, we can emulate the hand model by OpenGL. We use data-glove as our ground truth for comparison of the estimated joint angle. The experimental results show the influence of different distance from camera and different in-plane rotation angle. We prove that our finger joint estimation system is distance-insensitive and rotation-insensitive.