Abstract
In this thesis, we propose a real-time human full-body motion capturing system using the depth image from Kinect. Our system consists of three main steps to estimate human pose. First, we extract the characteristic landmarks on human body. By using pixel-based body part classifier, we segment the human silhouette into different body part regions. Then, we remove the outliers and extract the characteristic landmarks in the centers of body part regions. Second, we transform the landmarks to the feature vector with 3D position information. We apply the K-d tree to construct example-based system which will search several possible pose candidates. Third, we apply the voting to choose the best matching pose from candidates as the estimated pose. In experimental results, we prove that our system can operate in real-time and achieve sufficiently accuracy.