Situation-Adaptive Object Grasping Recognition in VR Environment
PubDate: December 2019
Teams: Saitama University
Writers: Koki Hirota; Takashi Komuro
In this paper, we propose a method for recognizing grasping of virtual objects in VR environment. The proposed method utilizes the fact that the position and shape of the virtual object to be grasped are known. A camera acquires an image of the user grasping a virtual object, and the posture of the hand is extracted from that image. The obtained hand posture is used to classify whether it is a grasping action or not. In order to evaluate the proposed method, we created a new dataset that was specialized for grasping virtual objects with a bare hand. There were three shapes and three positions of virtual objects in the dataset. The recognition rate of the classifier that was trained using the dataset with specific shapes of virtual objects was 93.18 %, and that with all the shapes of virtual objects was 87.71 %. This result shows that the recognition rate was improved by training the classifier using the shape-dependent dataset.