Indah Agustien Siradjuddin
University of Trunojoyo Madura

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Identification of Pedestrians Attributes Based on Multi-Class Multi-Label Classification using Convolutional Neural Network (CNN) Wrida Adi Wardana; Indah Agustien Siradjuddin; Arif Muntasa
Journal of Data Science and Its Applications Vol 3 No 1 (2020): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/jdsa.2020.3.43

Abstract

The usage of computer vision in identifying pedestrians attributes has received a great attention, especially in the visual surveillance systems. For instance, searching for system based on the attributes. Attributes Identification using Convolutional Neural Network architecture is presented in this article, since the architecture can perform feature learning. CNN consist of convolution layer, ReLU, Pooling, and Fully-connected. There are three experiment scenarios are conducted based on the number of convolution layers, to determine the effect of layers on CNN performance. Three different CNN architectures were trained and tested using a PETA dataset with 35 attributes. The highest accuracy achieved is 75.66% based on number of convolutional layers. The conducted experiments showed that more numbers of convolution layers used would produce the better CNN's performance.
Particle Filter with Gaussian Weighting for Human Tracking Indah Agustien Siradjuddin; M. Rahmat Widyanto; T. Basaruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 6: October 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Particle filter for object tracking could achieve high tracking accuracy.  To track the object, this method generates a number of particles which is the representation of the candidate target object.  The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weight.  Particle filter with Gaussian weighting is proposed to accomplish the computational problem.  There are two main stages in this method, i.e. prediction and update.  The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage.  In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle’s weight is calculated, and the remain particle’s weight is calculated using the Gaussian weighting.  Experiment is done using artificial dataset.  The average accuracy is 80,862%.  The high accuracy that is achieved by this method could use for the real time system tracking. DOI:  http://dx.doi.org/10.11591/telkomnika.v10i6.1187
Particle Filter with Gaussian Weighting for Human Tracking Indah Agustien Siradjuddin; M. Rahmat Widyanto; T. Basaruddin T. Basaruddin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 4: December 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i4.869

Abstract

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle’s weight. Particle filter with Gaussian weighting is proposed to accomplish the computational problem. There are two main stages in this method, i.e. prediction and update. The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage. In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle’s weight is calculated, and the remain particle’s weight is calculated using the Gaussian weighting. Experiment is done using artificial dataset. The average accuracy is 80,862%. The high accuracy that is achieved by this method could use for the real-time system tracking
Double Difference Motion Detection and Its Application for Madura Batik Virtual Fitting Room Rima Triwahyuningrum; Indah Agustien Siradjuddin; Yonathan Fery Hendrawan; Arik Kurniawati; Ari Kusumaningsih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2236

Abstract

Madura Batik Virtual Fitting Room using double difference algorithms motion detection is proposed in this research. This virtual fitting room consists of three main stages, i.e. motion detection, determination of region of interest of the detected motion, superimposed the virtual clothes into the region of interest. The double difference algorithm is used for the motion detection stage, since in this algorithm, the empty frame as the reference frame is not required. The double difference algorithm uses the previous and next frame to detect the motion in the current frame. Perception Test Images Sequences Dataset are used as the data of the experiment to measure the performance accuracy of this algorithm before the algorithm is used for the Madura batik virtual fitting room. The accuracy is 57.31%, 99.71%, and 78.52% for the sensitivity, specificity, and balanced accuracy, respectively. The build Madura batik virtual fitting room in this research can be used as the added feature of the Madura batik online stores, hence the consumer is able to see whether the clothes is fitted to them or not, and this virtual fitting room is also can be used as the promotion of Madura batik broadly.