This paper proposes human pose interpretation using particle filter (PF) with Binary Gaussian Weighting and support vector machine (SVM). In the proposed system, particle filter is used to track a human object, then this human object is skeletonized using thinning algorithm and classified using SVM. The classification is to identify human pose, whether it is normal or abnormal behavior. Here particle filter is modified through weight calculation using Gaussian distribution to reduce the computational time. The modified particle filter consists of four main phases. First, particles are generated to predict target’s location. Second, the weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, the weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The modified particle filter could reduce computational time of object tracking since this method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method builds Gaussian distribution and calculates particle’s weight using this distribution. Through an experiment using video data taken in front of the cashier of a convenience store, the proposed method reduced computational time in tracking process until 68.34% in average compared to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method, i.e. 90.3%. Combining particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.


Criminal data, http://www.tempointeraktif.com/hg/nasional/2005/01/01/brk,20050101-01,id.html, accessed on 2 November 2007.

Oblligation of using CCTV, http://www.ispy.co.id/mos/index.php?option=com_content&task=view&id= 43&Itemid=1, accessed on 15th April 2008.

I. Laptev, Object Detection with Boosted Histograms, IRISA/INRIA, Rennes, France, January 19, 2007.

C.Y. Suen, T.Y. Zhang, Communications of the ACM, 27/3 (1984) 236.

C. Fatihah, Tesis, Fakultas Ilmu Komputer, Universitas Indonesia, Indonesia, 2007.

M.S. Arumpalam, S. Maskell, N. Gordon, T. Clapp, IEEE Transaction on Signal Processing, 40/2 (2002) 174.

Y. Iwahori, T. Takai, H. Kawanaka, H. Itoh, Y. Adachi, Particle Filter Based Tracking of Moving Object from Image Sequence, KES 2006, Part II, LNAI 4252, Springer-Verlag, Berlin Heidelberg, 2006, p.401.

E.B. Meierm, F. Ade, Tracking Cars in Range Images Using Condensation Algorithm, Technical Report in Swiss Federal Institute of Technology, Zurich, Switzerland, 1999.

C.K. Ho, Short Introduction to Particle Filter, www.sps.ele.tue.nl/members/C.K.Ho/, accessed on February 2007.

K. Kawamoto, K. Hirota, N. Wakami, Efficient and Robust Curve Tracker based on Particle Filtering in Digital Space, ISIS, Korea, September 2005.

P. Fearnhead, Ph.D Thesis, University of Oxford, 1998.

N.J Gordon, D.J Salmon, A.F.M Smith, IEE Proceedings-F, 140/2 (1993) p.107.

K. Kawamoto, International Symposium on Advanced Intelligent System, Korea, September 2005.

I. Agustien, M.R. Widyanto, International Conference on Soft Computing, Intelligent System and Information Technology, Bali, Indonesia 2007.

I. Agustien, M.R. Widyanto, 4th International Conference Humanoid, Nanotechnology, Information Technology Communication and

Control, Environment and Management (HNICEM), Manila, Philipines, March 12-19, 2009.

C.K. Ho, Introduction to Particle Filter, Lecture Notes of High Tech Campus Eindhoven, Holland, 2005.

W.R. Leo, Statistics and the Treatment of Experimental Data, Adapted from Chapter 4, Technique for Nuclear and Particle Physics

Experiments, Springer-Verlag, Berlin Heidelberg, 1992, p.62.

M.R. Widyanto, S.N. Endah, K. Hirota, 4th International Conference Humanoid, Nanotechnology, Information Technology

Communication and Control, Environment and Management (HNICEM), Manila, Philipines, March 12-19, 2009.

B.E. Boser, I. Guyon, V. Vapnik, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, United States, ACM Press, 1992, p.144.

C. Cortes, V. Vapnik, Support-vector Network, Machine Learning 20 (1995) 273.

C. Hsu, C.C. Chang, C. Lin, A Practical Guide to Support Vector Classification, Department of Computer Science, National Taiwan University, Taiwan, 2008.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.