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Marifa Kurniasari
Universitas Negeri Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Mechanical Engineering

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Development of crowd detection warning system based on deep convolutional neural network using CCTV Muhammad Nurwidya Ardiansyah; Marifa Kurniasari; Muhammad Dzulfiqar Amien; Danang Wijaya; Pradana Setialana
Journal of Engineering and Applied Technology Vol 3, No 1 (2022): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v3i2.43771

Abstract

The 2019 corona virus (Covid-19) pandemic is a global problem for now. One way to deal with the spread of the corona virus is to maintain a distance of at least one meter and stay away from crowds. Therefore, a crowd detection warning system based on a deep convolutional neural network (deep CNN) was developed using CCTV. The development of this system was carried out using the NVIDIA Jetson Nano microcontroller as the computing hardware. Crowd object detection uses the OpenCV library, the YOLOv3-Tiny algorithm, and the euclidean distance method to calculate the distance between 'person' objects. Based on the tests carried out on function and performance, the results obtained that this crowd detection warning system can detect 'person' objects with an accuracy rate of 92.79. In addition, this system has also been able to detect several types of colors from objects so that warning messages can be given more specifically on the color of the clothes of the 'person' in the detected crowd.
Co-Authors Danang Wijaya Muhammad Dzulfiqar Amien Muhammad Nurwidya Ardiansyah Setialana, Pradana