Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons

Samridha Shrestha, Daohan Lu, Hanlin Tian, Qiming Cao, Julie Liu, John Ross Rizzo, William H. Seiple, Maurizio Porfiri, Yi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

During pandemics like COVID-19, social distancing is essential to combat the rise of infections. However, it is challenging for the visually impaired to practice social distancing as their low vision hinders them from maintaining a safe physical distance from other humans. In this paper, we propose a smartphone-based computationally-efficient deep neural network to detect crowds and relay the associated risks to the Blind or Visually Impaired (BVI) user through directional audio alerts. The system first detects humans and estimates their distances from the smartphone’s monocular camera feed. Then, the system clusters humans into crowds to generate density and distance maps from the crowd centers. Finally, the system tracks detections in previous frames creating motion maps predicting the motion of crowds to generate an appropriate audio alert. Active Crowd Analysis is designed for real-time smartphone use, utilizing the phone’s native hardware to ensure the BVI can safely maintain social distancing.

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2020 Workshops, Proceedings
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer Science and Business Media Deutschland GmbH
Pages422-439
Number of pages18
ISBN (Print)9783030668228
DOIs
StatePublished - 2020
EventWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: Aug 23 2020Aug 28 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12538 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshops held at the 16th European Conference on Computer Vision, ECCV 2020
CountryUnited Kingdom
CityGlasgow
Period8/23/208/28/20

Keywords

  • Active crowd analysis
  • Crowd density
  • Crowd distance
  • Crowd motion
  • Crowd-Risk Alert
  • Human detection
  • Pandemic risk mitigation
  • Visually impaired

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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