Deep Learning based Customer Count/Flow Monitoring System for Social Distancing

P. Zamorski, M. N. Asghar, L. Cooke, S. Daly, J. Francis, N. Kanwal, M. S. Ansari, E. Fallon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite the COVID-19 vaccination drives, use of preventative measures such as masks and social distancing are still deemed essential. This paper presents an application that will allow businesses/enterprises to monitor the flow of customers by detecting people as objects, counting the number of people, tracking the safe distance between them to maintain the two-meter distance norm. The proposed solution is set up to generate an alarm when the customers reach the allowed limit as per shop dimensions or overcrowding is detected. For the implementation, YOLOv4 and YOLOv3-Tiny were used for the task of object detection and transfer learning is used to set up weights. The models were evaluated using MSCOCO API with 100 image instances per class. The results of the YOLOv4 model are also compared with YOLOv3-Tiny in terms of calculating mean, average precision (AP), frames per second (FPS), and identification of groups (crowd). Experimental results (on several video clips from a shopping center CCTV) show that the YOLOv3-Tiny maintains real-time performance even on modest hardware. It is further demonstrated that if a high-end GPU is available, the overall detection of objects and cluster identification is much more accurate and clearer using YOLOv4.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages831-836
Number of pages6
ISBN (Electronic)9781665421744
ISBN (Print)9781665421744
DOIs
Publication statusPublished - 2021
Event19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 - Virtual, Online, Canada
Duration: 25 Oct 202128 Oct 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing and International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021

Conference

Conference19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021
Country/TerritoryCanada
CityVirtual, Online
Period25/10/2128/10/21

Keywords

  • COVID-19
  • Deep learning
  • Person detection
  • Physical distancing
  • YOLOv3-Tiny
  • YOLOv4

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