Vision-based human body posture recognition using support vector machines

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

*此作品的通信作者

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題iCAST 2012 - Proceedings
主出版物子標題4th International Conference on Awareness Science and Technology
頁面150-155
頁數6
DOIs
出版狀態Published - 2012
事件4th International Conference on Awareness Science and Technology, iCAST 2012 - Seoul, Korea, Republic of
持續時間: 21 8月 201224 8月 2012

出版系列

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

Conference

Conference4th International Conference on Awareness Science and Technology, iCAST 2012
國家/地區Korea, Republic of
城市Seoul
期間21/08/1224/08/12

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