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22 results on '"Park, Man-Woo"'

2. Augmented Reality Framework for Retrieving Information of Moving Objects on Construction Sites.

Author

Nguyen, Linh, Htet, Htoo Thiri, Lee, Yong-Ju, and Park, Man-Woo

Subjects
DIGITAL twins, OBJECT recognition (Computer vision), GLOBAL Positioning System, BUILDING sites, DIGITAL transformation
Abstract

The construction industry is undergoing a digital transformation, with the digital twin serving as a core system for project information. This digital twin provides an opportunity to utilize AR technology for real-time verification of on-site project information. Although many AR developments for construction sites have been attempted, they have been limited to accessing information on stationary components via Building Information Models. There have been no attempts to access information on dynamically changing resources, such as personnel and equipment. This paper addresses this gap by presenting an AR framework that enables site managers to verify real-time information on specific personnel or equipment. It introduces a matching algorithm for retrieving the necessary information from the digital twin. This algorithm is pivotal in identifying and retrieving the specific information needed from the vast dataset within the digital twin. The matching process integrates object detection and tracking algorithms applied to video frames from AR devices, along with GPS and IMU sensor data. Experimental results demonstrate the potential of this matching algorithm to streamline on-site management and reduce the effort required to interact with digital twin information. This paper highlights the transformative potential of AR and digital twin technologies in revolutionizing construction site operations. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Author

Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, Davari, Khashayar, Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, and Davari, Khashayar

Abstract

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters.

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19. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Author

Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, Davari, Khashayar, Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, and Davari, Khashayar

Abstract

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters.

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20. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Author

Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, Davari, Khashayar, Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, and Davari, Khashayar

Abstract

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters.

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21. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Author

Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, Davari, Khashayar, Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, and Davari, Khashayar

Abstract

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters.

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22. Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Author

Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, Davari, Khashayar, Zhu, Zhenhua, Park, Man-Woo, Koch, Christian, Soltani, Mohamad, Hammad, Amin, and Davari, Khashayar

Abstract

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters.

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