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Jun Yang

Researcher at Northwestern Polytechnical University

Publications -  13
Citations -  731

Jun Yang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Construction management & Tracking system. The author has an hindex of 9, co-authored 13 publications receiving 616 citations. Previous affiliations of Jun Yang include Georgia Institute of Technology.

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Construction performance monitoring via still images, time-lapse photos, and video streams

TL;DR: This paper extensively reviews these state-of-the-art vision-based construction performance monitoring methods and divides them into two categories (namely project level: visual monitoring of civil infrastructure or building elements vs. operation level:Visual monitoring of construction equipment and workers).
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Tracking multiple workers on construction sites using video cameras

TL;DR: The authors have developed a tracking algorithm based upon machine learning methods that requires several sample templates of the tracking target and learns a general model that can be applied to other targets with similar geometry.
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Vision-Based Tower Crane Tracking for Understanding Construction Activity

TL;DR: The use of a surveillance camera for assessing tower crane activities during the course of a workday is demonstrated to demonstrate that the crane jib trajectory, together with known information regarding the site plans, provides sufficient information to infer the activity states of the crane.
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Vision-based action recognition of construction workers using dense trajectories

TL;DR: Experimental results show that the system with codebook size 500 and MBH descriptor has achieved an average accuracy of 59% for worker action recognition, outperforming the state-of-the-art result by 24%.
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A performance evaluation of vision and radio frequency tracking methods for interacting workforce

TL;DR: The proposed visual tracking system is reliable and accurate enough to permit automated extraction of trajectory information for analysis purposes, such as would be required for work sampling and productivity assessments.