Title of article :
ADAPTIVE VISUAL GESTURE RECOGNITION FOR HUMAN-ROBOT INTERACTION
Author/Authors :
Hasanuzzaman, Mohammad University of Dhaka - Department of Computer Science and Engineering, Bangladesh , Tareeq, Saifuddin Mohammad University of Dhaka - Department of Computer Science and Engineering, Bangladesh , Zhang, Tao National Institute of Informatics - Intelligent Systems Research Division, Japan , Ampornaramveth, Vuthichai National Institute of Informatics - Intelligent Systems Research Division, Japan , Gotoda, Hironobu National Institute of Informatics - Intelligent Systems Research Division, Japan , Shirai, Yoshiaki Ritsumeikan University - School of Information Science and Engineering - Department of Human and Computer Intelligence, Japan , Ueno, Haruki National Institute of Informatics - Intelligent Systems Research Division, Japan
Abstract :
This paper presents an adaptive visual gesture recognition method for human–robot interaction using a knowledge-based software platform. The system is capable of recognizing users, static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. The system learns new users, poses using multi-cluster approach, and combines computer vision and knowledge-based approaches in order to adapt to new users, gestures and robot behaviors. In the proposed method, a frame-based knowledge model is defined for the person-centric gesture interpretation and human-robot interaction. It is implemented using the Frame-based Software Platform for Agent and Knowledge Management (SPAK). The effectiveness of this method has been demonstrated by an experimental human-robot interaction system using a humanoid robot, namely, ‘Robovie’.
Keywords :
Adaptive visual gesture recognition , human , robot interaction , multi , cluster based learning , SPAK.
Journal title :
Malaysian Journal of Computer Science
Journal title :
Malaysian Journal of Computer Science
