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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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dc.contributor.author Cho, Hyunjoong ko
dc.contributor.author Lee, Kyuiyong ko
dc.contributor.author Choi, Nakkwan ko
dc.contributor.author Kim, Seok ko
dc.contributor.author Lee, Jinhwi ko
dc.contributor.author Yang, Seungjoon ko
dc.date.available 2022-04-15T00:49:55Z -
dc.date.created 2022-04-11 ko
dc.date.issued 2022-04 ko
dc.identifier.citation IEEE ACCESS, v.10, pp.39769 - 39781 ko
dc.identifier.issn 2169-3536 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/58131 -
dc.description.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. ko
dc.language 영어 ko
dc.publisher Institute of Electrical and Electronics Engineers Inc. ko
dc.title Online Safety Zone Estimation and Violation Detection for Nonstationary Objects in Workplaces ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85128289972 ko
dc.type.rims ART ko
dc.identifier.doi 10.1109/access.2022.3165821 ko
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