다변량 퍼지 의사결정트리와 사용자 적응을 이용한 손동작 인식
Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation
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While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in KAIST[1]. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user’s hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.
Abstract
While increasing demand of the service for the disabled and the elderly people, assistive technologies have been developed rapidly. The natural signal of human such as voice or gesture has been applied to the system for assisting the disabled and the elderly people. As an example of such kind of human robot interface, the Soft Remote Control System has been developed by HWRS-ERC in KAIST[1]. This system is a vision-based hand gesture recognition system for controlling home appliances such as television, lamp and curtain. One of the most important technologies of the system is the hand gesture recognition algorithm. The frequently occurred problems which lower the recognition rate of hand gesture are inter-person variation and intra-person variation. Intra-person variation can be handled by inducing fuzzy concept. In this paper, we propose multivariate fuzzy decision tree(MFDT) learning and classification algorithm for hand motion recognition. To recognize hand gesture of a new user, the most proper recognition model among several well trained models is selected using model selection algorithm and incrementally adapted to the user’s hand gesture. For the general performance of MFDT as a classifier, we show classification rate using the benchmark data of the UCI repository. For the performance of hand gesture recognition, we tested using hand gesture data which is collected from 10 people for 15 days. The experimental results show that the classification and user adaptation performance of proposed algorithm is better than general fuzzy decision tree.
참고문헌 (13)
- Jun-Hyeong Do, "Soft Remote Control System in the Intelligent Sweet Home", Proc. of IEEE Int. conf. on IROS, pp.3984~3989 (2005)
- Sung Hoon Jung, "Adaptive Hand Motion Recognition in Soft Remote Control System", Proceedings of the 7th International Workshop on Human-friendly Welfare Robotic Systems, pp.22~24 (2006)
- 정성훈, "동작 유사도와 추이 예측을 이용한 한국 수화 인식에서의 사용자에 대한 적응", 석사, pp.2007
- 김대진, "신경망을 이용한 영상 기반 개인화 얼굴 표정 인식 시스템", 박사, pp.2004
- Moonjin Jeon, "Hand Motion Recognition using Fuzzy Decision Tree", Proceedings of the 8th International Workshop on Human-friendly Welfare Robotic Systems, pp.123~127 (2007)
- T. M. Mitchell, "Machine Learning" (1997), The McGraw-Hill
- Y.Kim,K.-H.Park,K.-H.Seo,C.H.Kim,W.-J.Lee,W.-G.Song,J.-H.Do,J.-J.Lee,B.K.Kim,J.-O.Kim,J.-T.Lim,and Z.Z.Bien, "report on questionnaire for developing intelligent sweet home for the disabled and the elderly in Korean living conditions", Proc. of the 8th Int. Conf. on Rehabilitation Robotics (ICORR 2003), Daejeon, Korea, pp.22~25 (2003)
- Cezary Z. Janikow, "Fuzzy Decision Trees : Issues and Methods", IEEE Transactions on Systems Man and Cybernetics, 28(1) (1998)
- Koen-Myung Lee, "A Fuzzy Decision Tree Induction Method for Fuzzy Data", A Fuzzy Decision Tree Induction Method for Fuzzy Data (1999)
- Olcay Taner Yildiz, "Linear Discriminant Trees", International Jounal of Pattern Recognition and Artificial Intelligence, 19(3), pp.323~353 (2005)
- Alpaydın, E., "Introduction to Machine Learning" (2004), The MIT Press
- H.-C. Fu, "User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks", IEEE Transactions on Neural Networks, 11(6), pp.1373~1384 (2000)
- "C. J. Merz and P. M. Murphy, “UCI repository for machine learning data-bases,” Department of Information and Computer Science. University of California, Irvine, 1996"[URL]