Authors:
Hajar Hiyadi
1
;
Fakhreddine Ababsa
2
;
Christophe Montagne
2
;
El Houssine Bouyakhf
3
and
Fakhita Regragui
3
Affiliations:
1
Evry Val d’Essonne University and Mohammed V University, France
;
2
Evry Val d’Essonne University, France
;
3
Mohammed V University, Morocco
Keyword(s):
3D Gesture Recognition, Gesture Tracking, Depth Image, Hidden Markov Models.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Human-Robots Interfaces
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Mobile Robots and Autonomous Systems
;
Robot Design, Development and Control
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vision, Recognition and Reconstruction
Abstract:
In this paper we propose a recognition technique of 3D dynamic gesture for human robot interaction (HRI) based on depth information provided by Kinect sensor. The body is tracked using the skeleton algorithm provided by the Kinect SDK. The main idea of this work is to compute the angles of the upper body joints which are active when executing gesture. The variation of these angles are used as inputs of Hidden Markov Models (HMM) in order to recognize the dynamic gestures. Results demonstrate the robustness of our method against environmental conditions such as illumination changes and scene complexity due to using depth information only.