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• W4283717422 endingPage "7934" @default.
• W4283717422 startingPage "7934" @default.
• W4283717422 abstract "In the study, an automated visualization of the proximity between workers and equipment is developed to manage workers’ safety at construction sites using the convolutional-neural-network-based image processing of a closed-circuit television video. The images are analyzed to automatically transform a hazard index visualized in the form of a plane map. The graphical representation of personalized proximity in the plane map is proposed and termed as safety ellipse in the study. The safety ellipse depending on the posture of workers and the area occupied by the hazardous objects (trucks) enable to represent precise proximity. Collision monitoring is automated with computer vision techniques of artificial-intelligence-based object detection, occupied space calculation, pose estimation, and homography." @default.
• W4283717422 created "2022-07-01" @default.
• W4283717422 creator A5069475973 @default.
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• W4283717422 date "2022-06-29" @default.
• W4283717422 modified "2023-09-26" @default.
• W4283717422 title "A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space" @default.
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• W4283717422 doi "https://doi.org/10.3390/su14137934" @default.
• W4283717422 hasPublicationYear "2022" @default.
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