Abstract
This contribution presents our approach for an instrumented automatic gesture recognition system for use in Augmented Reality, which is able to differentiate static and dynamic gestures. Basing on an infrared tracking system, infrared targets mounted at the users thumbs and index fingers are used to retrieve information about position and orientation of each finger. Our system receives this information and extracts static gestures by distance classifiers and dynamic gestures by statistical models. The concluded gesture is provided to any connected application. We introduce a small demonstration as basis for a short evaluation. In this we compare interaction in a real environment, Augmented Reality with a mouse/keyboard, and our gesture recognition system concerning properties, such as task execution time or intuitiveness of interaction. The results show that tasks executed by interaction with our gesture recognition system are faster than using the mouse/keyboard. However, this enhancement entails a slightly lowered wearing comfort.
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Buchmann, V., Violich, S., Billinghurst, M., Cockburn, A.: FingARtips: gesture based direct manipulation in Augmented Reality. In: GRAPHITE 2004: Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia (2004)
Advanced Realtime Tracking GmbH: ARTtrack1 (2005), http://www.ar-tracking.de
Stoerring, M., Moeslund, T.B., Liu, Y., Granum, E.: Computer Vision-Based Gesture Recognition for an Augmented Reality Interface. In: 4th IASTED International Conference on VISUALIZATION, IMAGING, AND IMAGE PROCESSING (2004)
Kaiser, E., Olwal, A., McGee, D., Benko, H., Corradini, A., Li, X., Cohen, P., Feiner, S.: Mutual disambiguation of 3D multimodal interaction in augmented and virtual reality. In: ICMI 2003: Proceedings of the 5th international conference on Multimodal interfaces (2003)
Wallhoff, F., Zobl, M., Rigoll, G.: Action Segmentation And Recognition in Meeting Room Scenarios. In: Proceedings on IEEE International Conference on Image Processing (2004)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In: Proceedings of the IEEE, vol. 77(2) (1989)
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© 2007 Springer-Verlag Berlin Heidelberg
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Reifinger, S., Wallhoff, F., Ablassmeier, M., Poitschke, T., Rigoll, G. (2007). Static and Dynamic Hand-Gesture Recognition for Augmented Reality Applications. In: Jacko, J.A. (eds) Human-Computer Interaction. HCI Intelligent Multimodal Interaction Environments. HCI 2007. Lecture Notes in Computer Science, vol 4552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73110-8_79
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DOI: https://doi.org/10.1007/978-3-540-73110-8_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73108-5
Online ISBN: 978-3-540-73110-8
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