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Yang, Seungjoon
Signal Processing Lab (SPL)
Research Interests
  • Statistical signal processing, multi-rate systems, image/video processing, computer vision

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Online Safety Zone Estimation and Violation Detection for Nonstationary Objects in Workplaces

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Title
Online Safety Zone Estimation and Violation Detection for Nonstationary Objects in Workplaces
Author
Cho, HyunjoongLee, KyuiyongChoi, NakkwanKim, SeokLee, JinhwiYang, Seungjoon
Issue Date
2022-04
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE ACCESS, v.10, pp.39769 - 39781
Abstract
This study presents a deep neural network (DNN)-based safety monitoring method. Nonstationary objects such as moving workers, heavy equipment, and pallets were detected, and their trajectories were tracked. Time-varying safety zones (SZs) of moving objects were estimated based on their trajectories, velocities, proceeding directions, and formations. SZ violations are defined by set operations with sets of points in the estimated SZs and the object trajectories. The proposed methods were tested using images acquired by CCTV cameras and virtual cameras in 3D simulations in plants and on loading docks. DNN-based detection and tracking provided accurate online estimation of time-varying SZs that were adequate for safety monitoring in the workplace. The set operation-based SZ violation definitions were flexible enough to monitor various violation scenarios that are currently monitored in workplaces. The proposed methods can be incorporated into existing site monitoring systems with single-view CCTV cameras at vantage points.
URI
https://scholarworks.unist.ac.kr/handle/201301/58131
DOI
10.1109/access.2022.3165821
ISSN
2169-3536
Appears in Collections:
EE_Journal Papers
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