A System to Interact with CAVE Applications Using Hand Gesture Recognition from Depth Data

Abstract

Human Computer Interaction (HCI) is a fundamental issue for virtual reality environments due to the need for natural approaches and comfortable devices. Such goals can be achieved using hand gestures to interact with the virtual reality engine. This paper presents a real-time system based on hand gesture recognition (HGR) for interaction with CAVE applications. The whole pipeline can be roughly divided into four steps: segmentation, feature extraction for bag-of-features construction, classification through multiclass support vector machine (SVM), generation of commands to control the application. We build a grammar based on the hand gesture classes to convert the classification results in control commands for an application running in a CAVE. The input is the depth stream data acquired from a Kinect device. The hand gesture recognition and command generation/execution approaches compose a client-server plug in that is part of a CAVE system implemented based on the Instant Reality architecture and the X3D standard. The results show that the implemented plug in is a promising solution. We achieve suitable recognition accuracy and efficient object manipulation in a virtual room representing a surgical environment visualized in the CAVE.