Upper-Body Pose Recognition Using Cylinder Pattern Model

Abstract

In this paper, we propose cylindrical coordinate system which can analyze upper-body pose of the depth images correctly. This method extracts the part of human body from the depth images, and we configure the center of the part as origin of the cylindrical coordinate system. And we configure multi-layered cylinders based on the origin, then, we can analyze pose using the pattern which is crossed depth images namely cylinders and upper-body. Since the crossed point of the cylinders and upper-body is obtained as brightness values, it can extract to convert feature vector of the cylindrical coordinate system. The extracted feature vectors of the cylindrical coordinate system are presented to feature space of circular and are categorized pose patterns. The pose patterns are learned using average value of the feature vectors, and the pose patterns are categorized as pose to compare to pre-defined pose patterns using Euclidean distance. In this paper, we applied dynamic cylinder model to the region of the upper-body, so we can be classified as head, body and arms through simple computation, and to extract pose information is possible effectively. In this paper, the effect that can get through proposing pose estimation method is as following. At the first, pose estimation is possible by using only minimum feature points which apply cylinder model in region that connect human's torso, head and arms. The second is as following. When we obtain the feature points, because of applying cylinder model, we can extract user's feature points and angle of rotation easily. And in this paper, we don't consider the status of the user's body is titled using only the upper-body poses of the state rightly standing pose toward the front. Because it has disadvantage which cannot distinguish between changes according to the tile of the torso, but we can detect the vectors of the hands and arm using cylindrical coordinate system. Therefore, in the future, we will study to be able to recognize the pose in the tilted status.