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

Paper:

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

Received:
April 23, 2009
Accepted:
July 23, 2009
Published:
January 20, 2010
Keywords:
support vector machine, thinning algorithm
Abstract

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.
Data files:
References
  1. [1] C. Fatihah, “Tesis: Boosting with Kernel Base Classifier for Human Object Detection,” Universitas Indonesia, Jakarta, 2007.
  2. [2] E. Hasting, “A Survey of Methodologies,” College of Engineering and Computer Science, University of Central Florida, Orlando.
  3. [3] C. Y. Suen and T. Y. Zhang, “A Fast Parallel Algorithm for Thinning Digital Patterns,” Communications of the ACM, Vol.27, No.3, March, 1984.
  4. [4] S. R. Gunn, “Technical Report : Support Vector Machine for Classification and Regression,” Faculty of Engineering, Science and Mathematics, University of Southampton, 14 May, 1998.
  5. [5] B. E. Boser, I. Guyon, and V. Vapnik. “A Training Algorithm for Optimal Margin Classifiers.” In Proc. of the Fifth Annual Workshop on Computational Learning Theory, pp. 144-152. ACM Press 1992.
  6. [6] C. Cortes and V. Vapnik, “Support — vector Network,” Machine Learning, 20, pp. 273-297, 1995.
  7. [7] C. Hsu, C. Chang, and C. Lin, “A Practical Guide to Support Vector Classification,” Department of Computer Science, National Taiwan University, Taiwan, 2008.
  8. [8] S. N. Endah and R. Widyanto, “Human Skeleton Pose Classification using Support Vector Machine,” Jurnal Ilmu Komputer dan Informasi, Fakultas Ilmu Komputer Universitas Indonesia, Vol.1-Nomor 2-Juni, pp. 27-30, 2008.

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Last updated on Dec. 13, 2018