Practical Automated Video Analytics for Crowd Monitoring and Counting

Kang Hao Cheong, Sandra Poeschmann, Joel Weijia Lai, Jin Ming Koh, U. Rajendra Acharya, Simon Ching Man Yu, Kenneth Jian Wei Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Video surveillance is gaining popularity in numerous applications, including facility management, traffic monitoring, crowd analysis, and urban security. Despite the increasing demand for closed-circuit television (CCTV) and related infrastructure in public spaces, there remains a notable lack of readily-deployable automated surveillance systems. In this study, we present a low-cost and efficient approach that integrates the use of computational object recognition to perform fully-automated identification, tracking, and counting of human traffic on camera video streams. Two software implementations are explored and the performance of these schemes is compared. Validation against controlled and non-controlled real-world environments is also demonstrated. The implementation provides automated video analytics for medium crowd density monitoring and tracking, eliminating labor-intensive tasks traditionally requiring human operation, with results indicating great reliability in real-life scenarios.

Original languageEnglish
Article number8926351
Pages (from-to)183252-183261
Number of pages10
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • background subtraction
  • counting
  • Crowd monitoring
  • data analytics
  • security
  • traffic monitoring

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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