Crowd and Group Detection in Shopping Centre Using Mask R-CNN

Lukas Purba Wisesa, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi

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

2 Citations (Scopus)

Abstract

This paper presents a mask R-CNN model and a dataset to detect crowd and group of people in one of the shopping areas in Indonesia. The dataset presented is the real condition of people behavior during Covid-19 pandemic. The dataset created have a total of 760 Pictures with more than 4500 total annotated objects classified in three classes (Person, Group, and Crowd) that is divided into train, validation, and test dataset. The model then created using Mask R-CNN with Resnet 101 Backbone using COCO Pre-weight. Using a dynamic learning rate, the model has an accuracy of 88.35% and 0.772 validation-loss while tested on test dataset. Our contribution includes creating a workflow of mask R-CNN implementation that could be implemented in various shopping centers and a Crowd detection model that can be use to create an automatic monitoring system.

Original languageEnglish
Title of host publication2022 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationAdvanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-79
Number of pages5
ISBN (Electronic)9781665460811
DOIs
Publication statusPublished - 2022
Event23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022 - Virtual, Surabaya, Indonesia
Duration: 20 Jul 202221 Jul 2022

Publication series

Name2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding

Conference

Conference23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period20/07/2221/07/22

Keywords

  • Computer-Vision
  • Covid-19
  • Crowd
  • Detection
  • Mask R-CNN

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