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).
[PDF]
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.
