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A hybrid ART-RBF Network Architecture for Viewpoint Invariant Human Activity Recognition

Htike@Muhammad Yusof, Zaw Zaw and Egerton, Simon and Kuang, Ye Chow (2010) A hybrid ART-RBF Network Architecture for Viewpoint Invariant Human Activity Recognition. Australian Journal of Intelligent Information Processing Systems, 12 (3). pp. 31-37. ISSN 1321-2133

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Abstract

There is growing interest in the problem of human activity recognition, motivated by its countless promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the infamous fixed viewpoint assumption and present a novel, efficient and biologically-inspired framework to recognize and classify human activities from monocular video source from arbitrary viewpoint. The proposed framework comprises two stages: human pose recognition and human activity recognition. We cascade an ensemble of invariant pose models and activity models hierarchically. All the models operate simultaneously in parallel and perform inference on impinging patterns that come from lower level. Pose models operate in a hybrid 3-layered bottom-up neural architecture. Activity models employ fuzzy-state hidden Markov model to classify activities. We have built a small-scale architecture for a proof-of-concept and performed some experiments on two publicly available datasets. The satisfactory experimental results demonstrate the efficacy of our framework and encourage us to further develop a full-scale architecture.

Item Type: Article (Journal)
Additional Information: Evidence attached
Uncontrolled Keywords: viewpoint invariant, human activity recognition, ART-RBF, fuzzy state HMM
Subjects: A General Works > AI Indexes (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr. Zaw Zaw Htike
Date Deposited: 05 Jun 2015 11:58
Last Modified: 05 Jun 2015 11:58
URI: http://irep.iium.edu.my/id/eprint/43205

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