Inferring Stochastic Regular Grammar with Nearness Information for Human Action Recognition

  • Kyungeun Cho
  • Hyungje Cho
  • Kyhyun Um
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


In this paper, we present an extended scheme of human action recognition with nearness information between hands and other body parts for the purpose of automatically analyzing nonverbal actions of human beings. First, based on the principle that a human action can be defined as a combination of multiple articulation movements, we apply the inference of stochastic grammars. We measure and quantize each human action in 3D coordinates and make two sets of 4-chain-code for xy and zy projection planes, so that they are appropriate for the stochastic grammar inference method. Next, we extend the stochastic grammar inferring method by applying nearness information. We confirm that various physical actions are correctly classified against a set of real-world 3D temporal data with this method in experiments. Our experiments show that this extended method reveals comparatively successful achievement with a 92.7% recognition rate of 60 movements of the upper body.


Recognition Rate Production Rule Dynamic Time Warping Finite Automaton Learning Pattern 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyungeun Cho
    • 1
  • Hyungje Cho
    • 1
  • Kyhyun Um
    • 1
  1. 1.Department of Computer and Multimedia EngineeringDongguk UniversitySeoulKorea

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