Earticle

초록

영어

To realize context aware applications for smart home environments, it is necessary to recognize function or usage of objects as well as categories of them. On conventional research for environment recognition in an indoor environment, most of previous methods are based on shape models. In this paper, we propose a method for recognizing objects focused on the relationship between human actions and functions of objects. Such relationship becomes obvious on human action patterns when he or she handles an object. To estimate object categories by using action patterns, we represent such relationship in Dynamic Bayesian Networks (DBNs). By learning human actions toward objects statistically, objects can be recognized. Finally, we performed experiments and confirmed that objects can be recognized from human actions without shape models.

목차

Abstract
 1. Introduction
 2. Overview.
 3. Extracting Features of Human Actions.
  3.1. Tracking Face and Hands
  3.2. Features of Human Action
 4. Recognizing Object Using DBNs
  4.1. A Typical DBN
  4.2. DBNs of Human Actions and Objects
  4.3. Observed and State Transition Probability Matrices
  4.4. DBNs of Object Regions
 5. Experiments
  5.1. Experiments without Overlapped Regions
  5.2. Experiments with Overlapped Region
  5.3. Discussions
 6. Conclusion
 References

키워드

저자

• Hiroshi Miki [ Osaka Prefecture University 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan ]
• Atsuhiro Kojima [ Osaka Prefecture University 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan ]
• Koichi Kise [ Osaka Prefecture University 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605