A full-body gesture database for automatic gesture recognition

Abstract

This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture (KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data at 3 different directions for normal and abnormal gestures. In case of command gestures, 2 pairs of stereo-video data is obtained by 2 sets of stereo cameras with different focal length in order to effectively capture views of whole body and upper body, simultaneously. In addition to these, the 2D silhouette data is synthesized by separating a subject and background in 2D stereo-video data and saved as binary mask images. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the way of obtaining the KUG database