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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Najwan Waisi
Northern Technical University
Computer Science & IT

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The detection of handguns from live-video in real-time based on deep learning Mohammed Ghazal; Najwan Waisi; Nawal Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.16174

Abstract

Many people have been killed indiscriminately by the use of handguns in different countries. Terroristacts, online fighting games and mentally disturbed people are considered the common reasons for these crimes.  A real-time handguns detection surveillance system is built to overcome these badacts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue,the designed detection system sends an alert message to the supervisor when aweapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgundetection operation. This model has been selected becauseit is fast and accurate in infering to integrate network for detecting and classifying weaponsin images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model bothenhance the accuracy level in identifying the real time handguns detection.
Pedestrian age estimation based on deep learning Nawal Younis Abdullah; Mohammed Talal Ghazal; Najwan Waisi
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1548-1555

Abstract

The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which have recently achieved the best performance in facial features extraction tasks: VGG-Face and ResNet-50. We combined these two models to increase the efficiency of the proposed system. We ran our experiments to evaluate the system based on the VGGFace2 dataset consisting of 3.31 million face images. From the experimental results, we observed a gap in the detection performances between those age groups: children from (00-10) years and elderly with 55 years and more. Moreover, it noted that the proposed pedestrian age estimation model performance is high, also a good result can be obtained by using the model for new purpose.
Co-Authors Mohammed Ghazal Mohammed Talal Ghazal Nawal Abdullah Nawal Younis Abdullah