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General Information
    • ISSN: 1793-8244 (Print)
    • Abbreviated Title:  J. Adv. Comput. Netw.
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Professor Haklin Kimm
    • Executive Editor: Ms. Cherry Chan
    • Abstracting/ Indexing: EBSCO, ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
Professor Haklin Kimm
East Stroudsburg University, USA
I'm happy to take on the position of editor in chief of JACN. We encourage authors to submit papers on all aspects of computer networks.

JACN 2014 Vol.2(1): 58-62 ISSN: 1793-8244
DOI: 10.7763/JACN.2014.V2.82

Automatic Classification of Human Body Postures Based on the Truncated SVD

N. Zerrouki and A. Houacine

Abstract—In this experimental study, we propose the use of Singular Value Decomposition (SVD) coefficients as features to automatically classify human body postures. The classification process uses images extracted from a fixed camera video. A background subtraction technique is applied for human body segmentation. A truncated SVD is performed by selecting significant magnitude coefficients. And the height-width ratio of the human body is also included in the set of features. The classification is then performed using an Artificial Neural Network (ANN). Four body postures are considered in our experiments, namely: standing, bending, sitting, and lying. Evaluation results show that the proposed method achieved 90.46% classification accuracy. Truncated SVD coefficients and height-width ratio as body posture features are thus appropriate descriptors to achieve high classification accuracy. Also, the proposed method yields the best classification accuracy compared to well-known classification methods.

Index Terms—Human body postures, classification, SVD coefficients, neural network.

The authors are with LCPTS laboratory, University of Sciences and Technology Houari Boumédienne, Algeria (e-mail: nzerrouki@usthb.dz, ahouacine@usthb.dz).


Cite:N. Zerrouki and A. Houacine, "Automatic Classification of Human Body Postures Based on the Truncated SVD," Journal of Advances in Computer Networks vol. 2, no. 1, pp. 58-62, 2014.

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