MAKARA Journal of Technology Series
Vol 14, No 1 (2010): April

PARTICLE FILTER WITH BINARY GAUSSIAN WEIGHTING AND SUPPORT VECTOR MACHINE FOR HUMAN POSE INTERPRETATION

Indah Agustien (Faculty of Engineering, University of Trunojoyo, Bangkalan-Madura 16912, Indonesia)
Muhammad Rahmat Widyanto (Faculty of Computers Science, University of Indonesia, Depok 16424, Indonesia)
Sukmawati Endah (Faculty of Mathematics and Natural Science, Diponegoro University, Tembalang-Semarang 50239, Indonesia)
Tarzan Basaruddin (Faculty of Computers Science, University of Indonesia, Depok 16424, Indonesia)



Article Info

Publish Date
14 Oct 2010

Abstract

Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine isproposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonizedusing thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose,whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce the computational time. The modified particle filter consists of four main phases. First, particlesare generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used tobuild Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, updateparticles based on each weight. The modified particle filter could reduce computational time of object tracking sincethis method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method buildsGaussian distribution and calculates particle’s weight using this distribution. Through experiment using video datataken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method iscomparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting andsupport vector machine is promising for advanced early crime scene investigation.Keywords: particle filter, prediction, skeletonization, support vector machine, update

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