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

doi:10.1007/978-3-030-66823-5_25

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 proceeding › Conference 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 language	English (US)
Title of host publication	Computer Vision – ECCV 2020 Workshops, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	422-439
Number of pages	18
ISBN (Print)	9783030668228
DOIs	https://doi.org/10.1007/978-3-030-66823-5_25
State	Published - 2020
Event	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: Aug 23 2020 → Aug 28 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12538 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	8/23/20 → 8/28/20

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
General Computer Science

Access to Document

10.1007/978-3-030-66823-5_25

Cite this

Shrestha, S., Lu, D., Tian, H., Cao, Q., Liu, J., Rizzo, J. R., Seiple, W. H., Porfiri, M., & Fang, Y. (2020). Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops, Proceedings (pp. 422-439). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12538 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-66823-5_25

Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons. / Shrestha, Samridha; Lu, Daohan; Tian, Hanlin et al.
Computer Vision – ECCV 2020 Workshops, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. p. 422-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12538 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shrestha, S, Lu, D, Tian, H, Cao, Q, Liu, J, Rizzo, JR, Seiple, WH, Porfiri, M & Fang, Y 2020, Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12538 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 422-439, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 8/23/20. https://doi.org/10.1007/978-3-030-66823-5_25

Shrestha S, Lu D, Tian H, Cao Q, Liu J, Rizzo JR et al. Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons. In Bartoli A, Fusiello A, editors, Computer Vision – ECCV 2020 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 422-439. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-66823-5_25

Shrestha, Samridha ; Lu, Daohan ; Tian, Hanlin et al. / Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons. Computer Vision – ECCV 2020 Workshops, Proceedings. editor / Adrien Bartoli ; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. pp. 422-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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