Indonesian Journal of Electrical Engineering and Computer Science
Vol 26, No 2: May 2022

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Iqbal Ahmad Dahlan (Universitas Pertahanan Republik Indonesia)
Muhammad Bryan Gutomo Putra (Institut Teknologi Bandung)
Suhono Harso Supangkat (Institut Teknologi Bandung)
Fadhil Hidayat (Institut Teknologi Bandung)
Fetty Fitriyanti Lubis (Institut Teknologi Bandung)
Faqih Hamami (Telkom University)



Article Info

Publish Date
01 May 2022

Abstract

Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.

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