Vision-based Crowd Counting and Social Distancing Monitoring using Tiny-YOLOv4 and DeepSORT

With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The...

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Published inIEEE ... International Smart Cities Conference (Online) pp. 1 - 7
Main Authors Valencia, Immanuel Jose C., Dadios, Elmer P., Fillone, Alexis M., Puno, John Carlo V., Baldovino, Renann G., Billones, Robert Kerwin C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.09.2021
Subjects
Cameras
computer vision
crowd density
Decision making
Density measurement
Human factors
monitoring
Protocols
Smart cities
social distancing
Surveillance
Online AccessGet full text
ISSN2687-8860
DOI10.1109/ISC253183.2021.9562868

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Abstract With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The application is able to process video files or live camera feed such as CCTV or surveillance cameras and generate reports indicating people detected per unit time, percentage of social distancing per unit time, detection and social distancing logs as well as color-coded bounding boxes to indicate if the detected people are following social distancing protocols.
AbstractList With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application that utilizes Tiny-YOLOv4 and DeepSORT tracking algorithm to monitor crowd count and social distancing in a top-view camera perspective. The application is able to process video files or live camera feed such as CCTV or surveillance cameras and generate reports indicating people detected per unit time, percentage of social distancing per unit time, detection and social distancing logs as well as color-coded bounding boxes to indicate if the detected people are following social distancing protocols.
Author Fillone, Alexis M.
Dadios, Elmer P.
Billones, Robert Kerwin C.
Baldovino, Renann G.
Puno, John Carlo V.
Valencia, Immanuel Jose C.
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  givenname: Robert Kerwin C.
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  email: robert.billones@dlsu.edu.ph
  organization: De La Salle University,Manufacturing Engineering and Management Department,Manila,Philippines
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Snippet With the novel coronavirus, social distancing and crowd monitoring became vital in managing the spread of the virus. This paper presents a desktop application...
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SubjectTerms Cameras
computer vision
crowd density
Decision making
Density measurement
Human factors
monitoring
Protocols
Smart cities
social distancing
Surveillance
Title Vision-based Crowd Counting and Social Distancing Monitoring using Tiny-YOLOv4 and DeepSORT
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