Abstract
We introduce a novel approach to gesture recognition, based on Pseudo 3D Hidden Markov Models. This technique is capable of integrating spatially and temporally derived features in an elegant way, thus making possible the recognition of static gestures such as standing in a special posture, as well as dynamic gestures such as hand waving. Pseudo 2D Hidden Markov Models have been utilized for two dimensional Problems such as face recognition. P3DHMMs can be considered as an extension of 2D case, where the so-called superstates in P3DHMM encapsulate P2DHMMs. By the means of this structure, image sequences can be generated by the model. The Performance of our approach is demonstrated in this paper by a number of experiments on a gesture database of nine different predefined gestures.
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© 2000 Springer-Verlag Berlin Heidelberg
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Yalcin, I.K., Kilinc, A.T., Müller, S., Rigoll, G. (2000). Gesture Recognition Using Pseudo 3D Hidden Markov Models. In: Sommer, G., Krüger, N., Perwass, C. (eds) Mustererkennung 2000. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59802-9_53
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DOI: https://doi.org/10.1007/978-3-642-59802-9_53
Publisher Name: Springer, Berlin, Heidelberg
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