Hierarchical recognition of daily human actions based on continuous hidden Markov models

  • Authors:

  • Taketoshi Mori;Yushi Segawa;Masamichi Shimosaka;Tomomasa Sato

  • Affiliations:

  • The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan

  • Venue:
  • FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
  • Year:
  • 2004

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Abstract

This paper presents a recognition method of human daily-life action. The method utilizes hierarchical structure of actions and describes it as tree. We modelize actions by Continuous Hidden Markov Models which output time-series feature vectors extracted by Feature Extraction Filter based on knowledge of human. In this method, recognition starts from the root, competes the likelihoods of child-nodes, chooses the maximum one as recognition result of the level, and goes to deeper level. The advantages of hierarchical recognition are:1)recognition of various levels of abstraction, 2)simplification of low-level models, 3)response to novel data by decreasing degree of details. Experimental result shows that the method is able to recognize some basic human actions.