3D Gesture Recognition Applying Long Short-Term Memory and Contextual Knowledge in a CAVE

摘要

Virtual reality applications are emerging into various regions of research and entertainment. Although visual and acoustic capabilities are already quite impressive, a wide range of users still criticizes the user interface. Frequently complex and very sensitive input devices are being used, although simple gestures would be preferred. While gesture recognition systems are quite common, see Nintendo's Wii mote, a CAVE has further challenges, as the person can be located in any random position and the gestures are not being performed related to a common fixpoint. Applying an infrared tracking system it is possible to reliably locate the hand and compute 3D trajectories. These are then further analyzed with a Long Short-Term Memory approach, which is able to model sequences of variable length with a higher reliability than HMMs.