Social Distancing Crowd Segmentation, Estimation and Visualisation
11th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2021
; 2021-November, 2021.
Article
in English
| Scopus | ID: covidwho-1767005
ABSTRACT
This research shows a modern crowd counting solution which alters typical prediction solutions into a segmentation of individuals based on a distance threshold, allowing for better visualisation and results. The study proposes using YOLOv4-normal and YOLOv4-tiny models, which have shown great results throughout calibration with an MAE of 14 and 36 respectively. However it did present some issues of accuracy degradation when trained on head annotations at any level of crowd density. As for visualisation, perspective transformation was used which directly helped in providing the distance calculation that was absent from standard transformation. If any variants of YOLOv4 are to be used, the main argument is the choice between speed over accuracy while relying on native implementations. In the case of distance regulation, any transformation that maps itself onto the region of interest, such as perspective transformation should be used to precisely determine distances from a camera to the region of interest itself. © 2021 IEEE.
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Database:
Scopus
Language:
English
Journal:
11th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2021
Year:
2021
Document Type:
Article
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