Tarzan Basaruddin, Tarzan
Fakultas Ilmu Komputer, Universitas Indonesia, Depok 16424, Indonesia

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Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation Agustien, Indah; Widyanto, Muhammad Rahmat; Endah, Sukmawati; Basaruddin, Tarzan
Makara Journal of Technology Vol 14, No 1 (2010)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (142.461 KB) | DOI: 10.7454/mst.v14i1.174

Abstract

Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine is proposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonized using 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, particles are generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, update particles 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 experiment using video data taken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until 68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.
On the Performance of SVD-DWT Based Digital Video Watermarking Technique with Semi-Blind Detector Basaruddin, Tarzan; Maulidiya, Della
Makara Journal of Technology Vol 13, No 1 (2009)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1945.229 KB) | DOI: 10.7454/mst.v13i1.153

Abstract

This paper presents a watermarking technique for digital video. The proposed scheme is developed based on the work of Ganic and Chan which took the virtue of SVD and DWT. While the previous works of Chan has the blind detector property, our attempt is to develop a scheme with semi-blind detector, by using the merit of the DWT-SDV technique proposed by Ganic which was originally applied to still image. Overall, our experimental results show that our proposed scheme has a very good imperceptibility and is reasonably robust especially under several attacks such as compression, blurring, cropping, and sharpening.