Extension of Hidden Markov models for multiple candidates and its application to gesture recognition

Yosuke Sato*, Tetsuji Ogawa, Tetsunori Kobayashi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We propose a modified Hidden Markov Model (HMM) with a view to improve gesture recognition using a moving camera. The conventional HMM is formulated so as to deal with only one feature candidate per frame. However, for a mobile robot, the background and the lighting conditions are always changing, and the feature extraction problem becomes difficult. It is almost impossible to extract a reliable feature vector under such conditions. In this paper, we define a new gesture recognition framework in which multiple candidates of feature vectors are generated with confidence measures and the HMM is extended to deal with these multiple feature vectors. Experimental results comparing the proposed system with feature vectors based on DCT and the method of selecting only one candidate feature point verifies the effectiveness of the proposed technique.

Original languageEnglish
Pages (from-to)1239-1246
Number of pages8
JournalIEICE Transactions on Information and Systems
Issue number6
Publication statusPublished - 2005 Jun


  • Gesture recognition
  • Hidden Markov Model
  • Mobile robot
  • Multiple candidates of feature vector

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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