Applications of HMM modeling to recognizing human gestures in image sequences for a man-machine interface

Perry A. Stoll, Jun Ohya

研究成果: Paper査読

12 被引用数 (Scopus)

抄録

Efforts to understand human motion have been increasing in number and complexity, and will most likely prove to be a key component in human-computer interfaces. One key feature of motion in general, and human motion in particular, is its dynamic nature. The present work seeks to model human motions in a manner amenable to leaning and recognition. Toward this end, hidden Markov models (HMMs) are employed to model semantically meaningful human movements. The data used for modeling the human motions is an approximate pose derived from a sequence of camera images. An HMM is learned for each motion class and employed as a maximum likelihood recognizer. Experiments show promising results for a set of six sport actions.

本文言語English
ページ129-134
ページ数6
出版ステータスPublished - 1995 12 1
外部発表はい
イベントProceedings of the 1995 4th IEEE International Workshop on Robot and Human Communication, RO-MAN - Tokyo, Jpn
継続期間: 1995 7 51995 7 7

Other

OtherProceedings of the 1995 4th IEEE International Workshop on Robot and Human Communication, RO-MAN
CityTokyo, Jpn
Period95/7/595/7/7

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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