JACIII Vol.14 No.1 pp. 28-33
doi: 10.20965/jaciii.2010.p0028


Human Behavior Classification Using Thinning Algorithm and Support Vector Machine

M. Rahmat Widyanto*, Sukmawati Nur Endah**, and Kaoru Hirota***

*Faculty of Computer Science, University of Indonesia Depok 16424, Indonesia

**Faculty of Mathematics and Natural Science, Diponegoro University Prof. Soedarto, S.H. street, Tembalang, Semarang, Indonesia

***Dept. Computational Intelligence & System Science, Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

April 23, 2009
July 23, 2009
January 20, 2010
support vector machine, thinning algorithm

This paper proposes a skeleton-based human behavior classification system using thinning algorithm and Support Vector Machine (SVM). The proposed system consists of two phases, skeletonization phase where main human body part is constructed using thinning algorithm, and classification phase where the skeleton constructed by previous phase is classified into certain human behavior pose using SVM. Experiment using 44 training and 44 testing data of real human poses shows that the system achieves 81.06% accuracy. This system can be further developed for early detection of criminal action.

Cite this article as:
M. Widyanto, S. Endah, and K. Hirota, “Human Behavior Classification Using Thinning Algorithm and Support Vector Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.1, pp. 28-33, 2010.
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Last updated on Dec. 13, 2018