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A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space

Author

Listed:
  • Yoon-Soo Shin

    (Department of Architectural Engineering, Dankook University, Yongin 16890, Korea)

  • Junhee Kim

    (Department of Architectural Engineering, Dankook University, Yongin 16890, Korea)

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.

Suggested Citation

  • Yoon-Soo Shin & Junhee Kim, 2022. "A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space," Sustainability, MDPI, vol. 14(13), pages 1-13, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7934-:d:851666
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    References listed on IDEAS

    as
    1. Shraddha Palikhe & Jae Young Lee & Bubryur Kim & Mi Yirong & Dong-Eun Lee, 2022. "Ergonomic Risk Assessment of Aluminum Form Workers’ Musculoskeletal Disorder at Construction Workstations Using Simulation," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    2. Paige Wenbin Tien & Shuangyu Wei & John Calautit, 2020. "A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand," Energies, MDPI, vol. 14(1), pages 1-28, December.
    3. Hyunsoo Kim & Sangwon Han, 2018. "Accuracy Improvement of Real-Time Location Tracking for Construction Workers," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    4. Qijun Hu & Yu Bai & Leping He & Jie Huang & Haoyu Wang & Guangran Cheng, 2022. "Workers’ Unsafe Actions When Working at Heights: Detecting from Images," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    5. Yunsick Sung, 2016. "RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments," Sustainability, MDPI, vol. 8(11), pages 1-9, November.
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