Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8

Computer simulations using agent-based approach aimed at modeling human behavior require a robust dataset derived from actual observation to serve as ground truth. This paper details an approach for developing a movement behavior dataset generator from CCTV footages with respect to two health-relate...

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Main Authors: Abao, Roland P., Estuar, Ma. Regina Justina, Abu, Patricia Angela R
Format: text
Published: Archīum Ateneo 2023
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/393
https://doi.org/10.1007/978-3-031-43129-6_29
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-13932024-02-20T09:26:04Z Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8 Abao, Roland P. Estuar, Ma. Regina Justina Abu, Patricia Angela R Computer simulations using agent-based approach aimed at modeling human behavior require a robust dataset derived from actual observation to serve as ground truth. This paper details an approach for developing a movement behavior dataset generator from CCTV footages with respect to two health-related behaviors: face mask wearing and physical distancing, while addressing the privacy concerns of confidential CCTV data. A two-stage YOLOv8-based cascaded approach was implemented for object tracking and detection. The first stage involves tracking of individuals in the video feed to determine physical distancing behavior using the pre-trained YOLOv8 xLarge model paired with Bot-SORT multi-object tracker and OpenCV Perspective-n-Point pose estimation. The second stage involves determining the mask wearing behavior of the tracked individuals using the best-performing model among the five YOLOv8 models (nano, small, medium, large, and xLarge), each trained for 50 epochs on a custom CCTV dataset. Results show that the custom-trained xLarge model performed the best on the mask detection task with the following metric scores: mAP50 = 0.94; mAP50-95 = 0.63; and F1 = 0.872. The faces of all the tracked individuals are blurred-out in the resulting video frames to preserve the privacy of the CCTV data. Finally, the developed system is able to generate the corresponding mask-distancing behavior dataset and annotated output videos from the input CCTV raw footages. 2023-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/393 https://doi.org/10.1007/978-3-031-43129-6_29 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo CCTV data dataset generator mask wearing physical distancing YOLOv8 Computer Engineering Electrical and Computer Engineering Engineering
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic CCTV data
dataset generator
mask wearing
physical distancing
YOLOv8
Computer Engineering
Electrical and Computer Engineering
Engineering
spellingShingle CCTV data
dataset generator
mask wearing
physical distancing
YOLOv8
Computer Engineering
Electrical and Computer Engineering
Engineering
Abao, Roland P.
Estuar, Ma. Regina Justina
Abu, Patricia Angela R
Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
description Computer simulations using agent-based approach aimed at modeling human behavior require a robust dataset derived from actual observation to serve as ground truth. This paper details an approach for developing a movement behavior dataset generator from CCTV footages with respect to two health-related behaviors: face mask wearing and physical distancing, while addressing the privacy concerns of confidential CCTV data. A two-stage YOLOv8-based cascaded approach was implemented for object tracking and detection. The first stage involves tracking of individuals in the video feed to determine physical distancing behavior using the pre-trained YOLOv8 xLarge model paired with Bot-SORT multi-object tracker and OpenCV Perspective-n-Point pose estimation. The second stage involves determining the mask wearing behavior of the tracked individuals using the best-performing model among the five YOLOv8 models (nano, small, medium, large, and xLarge), each trained for 50 epochs on a custom CCTV dataset. Results show that the custom-trained xLarge model performed the best on the mask detection task with the following metric scores: mAP50 = 0.94; mAP50-95 = 0.63; and F1 = 0.872. The faces of all the tracked individuals are blurred-out in the resulting video frames to preserve the privacy of the CCTV data. Finally, the developed system is able to generate the corresponding mask-distancing behavior dataset and annotated output videos from the input CCTV raw footages.
format text
author Abao, Roland P.
Estuar, Ma. Regina Justina
Abu, Patricia Angela R
author_facet Abao, Roland P.
Estuar, Ma. Regina Justina
Abu, Patricia Angela R
author_sort Abao, Roland P.
title Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
title_short Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
title_full Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
title_fullStr Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
title_full_unstemmed Physical Distancing and Mask Wearing Behavior Dataset Generator from CCTV Footages Using YOLOv8
title_sort physical distancing and mask wearing behavior dataset generator from cctv footages using yolov8
publisher Archīum Ateneo
publishDate 2023
url https://archium.ateneo.edu/discs-faculty-pubs/393
https://doi.org/10.1007/978-3-031-43129-6_29
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