Toward natural interaction in the real world: real-time gesture recognition
Author(s)
Yin, Ying; Davis, Randall
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Using a new hand tracking technology capable of tracking 3D hand postures in real-time, we developed a recognition system for continuous natural gestures. By natural gestures, we mean those encountered in spontaneous interaction, rather than a set of artificial gestures chosen to simplify recognition. To date we have achieved 95.6% accuracy on isolated gesture recognition, and 73% recognition rate on continuous gesture recognition, with data from 3 users and twelve gesture classes. We connected our gesture recognition system to Google Earth, enabling real time gestural control of a 3D map. We describe the challenges of signal accuracy and signal interpretation presented by working in a real-world environment, and detail how we overcame them.
Date issued
2010-11Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
Proceeding of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI '10)
Publisher
Association for Computing Machinery (ACM)
Citation
Yin, Ying, and Randall Davis. “Toward natural interaction in the real world.” ACM Press, 2010.
Version: Author's final manuscript
ISBN
978-1-4503-0414-6