Abstract
We have developed a real-time gesture recognition system whose models can be taught by only one instruction. Therefore the system can adapt to new gesture performer quickly but it can not raise the recognition rates even if we teach gestures many times. That is because the system could not utilize all the teaching data. In order to cope with the problem, averages of teaching data are calculated. First, the best frame correspondence of the teaching data and the model is obtained by Continuous DP. Next the averages and variations are calculated for each frame of the model. We show the effectiveness of our method in the experiments.
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Takuichi Nishimura: He is a researcher of Multi-modal Function Tsukuba Laboratory and Information Basis Function Laboratory at the Real World Computing Partnership. He has engaged in motion image understanding, multi-modal human computer interface, multi-modal information retrieval, and mobile robot navigation. He completed the master’s course of the University of Tokyo in 1992.
Hiroaki Yabe: He is from SHARP corporation working as a researcher of Multi-modal Function Tsukuba Laboratory and Information Basis Function Tsukuba Laboratory at the Real World Computing Partnership. He has engaged in motion image understanding, multi-modal human computer interface, multi-modal information retrieval. He completed the master’s course of the University of Tokyo in 1995.
Ryuichi Oka, Ph.D.: He is a chief of Multi-modal Function Tsukuba Laboratory and Information Basis Function Laboratory at Tsukuba Research Center of the Real World Computing Partnership (RWC Japan) which started in 1992. His research interests include motion image understanding, spontaneous speech understanding, self-organisation information base, multi-modal human computer interface, multi-modal information retrieval, mobile robot, integration of symbol and pattern, and super parallel computation. He received his Ph.D degree in Engineering from the University of Tokyo.
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Nishimura, T., Yabe, H. & Oka, R. A method of model improvement for spotting recognition of gestures using an image sequence. New Gener Comput 18, 89–101 (2000). https://doi.org/10.1007/BF03037588
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DOI: https://doi.org/10.1007/BF03037588