Abstract
In this paper, we present a new method of recognizing human actions by inference of stochastic grammars for the purpose of automatic analysis of nonverbal actions of human beings. We applied the principle that a human action can be defined as a combination of multiple articulation movements. We measure and quantize each articulation movements in 3D and represent two sets of 4-connected chain code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. This recognition method is tested by using 900 actions of human upper body. The result shows a comparatively successful achievement of 93.8% recognition rate through the experiments of 8 action types of head and 84.9% recognition rate of 60 action types of upper body.
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© 2004 Springer-Verlag Berlin Heidelberg
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Cho, K., Cho, H., Um, K. (2004). Human Action Recognition by Inference of Stochastic Regular Grammars. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_41
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DOI: https://doi.org/10.1007/978-3-540-27868-9_41
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