Vision-based human body posture recognition using support vector machines

Chia Feng Juang*, Chung Wei Liang, Chiung Ling Lee, I. Fang Chung

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This paper proposes a vision-based human posture recognition method using a support vector machine (SVM) classifier. Recognition of four main body postures is considered in this paper, and they are standing, bending, sitting, and lying postures. First of all, two cameras are used to capture two sets of image sequences at the same time. After capturing the image sequences, a RGB-based moving object segmentation algorithm is used to distinguish the human body from background. Two complete and corresponding silhouettes of the human body are obtained. The Discrete Fourier Transform (DFT) coefficients and length-width ratio are calculated from horizontal and vertical projections of each silhouette. Finally, these features are fed to a Gaussian-kernel-based SVM to recognize postures. Experimental results show that the proposed method achieves a high recognition rate.

Original languageEnglish
Title of host publicationiCAST 2012 - Proceedings
Subtitle of host publication4th International Conference on Awareness Science and Technology
Pages150-155
Number of pages6
DOIs
StatePublished - 2012
Event4th International Conference on Awareness Science and Technology, iCAST 2012 - Seoul, Korea, Republic of
Duration: 21 Aug 201224 Aug 2012

Publication series

NameiCAST 2012 - Proceedings: 4th International Conference on Awareness Science and Technology

Conference

Conference4th International Conference on Awareness Science and Technology, iCAST 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period21/08/1224/08/12

Keywords

  • computer vision
  • discrete fourier transform
  • object segmentation
  • posture recognition
  • support vector machines

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