Recognition of human motion from qualitative normalised templates
Document Type
Article
Publication Date
1-1-2007
Abstract
This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching algorithm (e.g. template matching) with the proposed QNTs. The human motion is modelled by the time-varying joint angles and link lengths of an articulated human model. The ability to manage the trade-offs between model complexity and computational cost plays a crucial role in the performance of human motion classification. The QNTs is developed to categorise complex human motion into sets of fuzzy qualitative angles and positions in quantity space. Classification of the human motion is done by comparing the QNTs to the parameters learned from numerical motion tracking. Experimental results have demonstrated the effectiveness of our proposed method when classifying simple human motions, e.g. running and walking.
Keywords
Human motion classification, pattern recognition
Divisions
ai
Publication Title
Journal of Intelligent & Robotic Systems
Volume
48
Issue
1
Additional Information
Department of Artificial Intelligence, Faculty of Computer Science & Information Technology University of Malaya, 50603 Kuala Lumpur, MALAYSIA