Automatic Classification of Human Body Postures Based on the Truncated SVD
Nabil Zerrouki, Amrane Houacine · Journal of Advances in Computer Networks · 2014
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.