"Physical Distancing and Mask Wearing Behavior Dataset Generator from C" by Roland P. Abao, Ma. Regina Justina Estuar et al.
 

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

Document Type

Conference Proceeding

Publication Date

1-1-2023

Abstract

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.

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