Adaptive-Neuro Fuzzy Inference System for Human Posture Classification Using a Simplified Shock Graph

Authors:
S. Shahbudin;A. Hussain;Ahmed El-Shafie;N. M. Tahir;S. A. Samad
Affiliations:
Department of Electrical, Electronics & Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia;Department of Electrical, Electronics & Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia;Department of Electrical, Electronics & Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia;Department of Computer, Faculty of Electrical Engineering, Technology University of Mara, Shah Alam, Malaysia;Department of Electrical, Electronics & Systems Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia, Bangi, Malaysia
Venue:
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Year:
2009

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Abstract

In this paper, a neuro-fuzzy technique known as the Adaptive-Neuro Fuzzy Inference System (ANFIS) has been used to highlight the application of ANFIS to perform human posture classification task using the new simplified shock graph (SSG) representation. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts and organized in a hierarchical, directed acyclic graph is used as a powerful representation of human shape in our work. The SSG feature provides a compact, unique and simple way of representing human shape and has been tested with several classifiers. As such, in this paper we intend to test its efficacy with another classifier, that is, the ANFIS classifier system. The result showed that the proposed ANFIS model can be used in classifying various human postures.