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

3. 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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12. Improved Localization of Construction Workers in Video Frames by Integrating Detection and Tracking

Author

Brilakis, Ioannis and Park, Man-Woo

Abstract

Tracking of construction site resources from on-site cameras using vision trackers has been proposed as a means to efficiently track a large number of objects operating in a congested environment. The systems proposed so far use first either a less accurate/faster motion detection approach or a more sophisticated object detection approach to mark the moving entities of interest in the camera view. The marked entities are then passed on to a vision tracker, who follows the entities in all subsequent frames. The major drawback of this two-step process is that the resulting tracking systems are very sensitive to changes and, in most cases, deteriorate and stop tracking effectively after only a few seconds/minutes. The reason is that it is common for construction resources to frequently a) enter/leave the field of view, b) be temporarily occluded, or c) significantly change their pose. To resolve this issue, this paper presents a hybrid method for locating construction workers that interchangeably fuses the methods of tracking and object detection. Specifically, the proposed approach continuously detects construction workers, and tracks them. Continuous detection allows a) for the detection of workers that enter the view or reappear after being occluded at any given time, b) for the effective termination of tracking when workers leave the view or become occluded, and c) for relocating the marked area and detection confidence to fit all object poses. On the other hand, continuous tracking allows for seamless tracking across time and smoothing of the temporal detection variations. The proposed approach was implemented into a C# prototype and tested on videos taken from construction sites in Atlanta, GA. The method could detect and track all workers, preserving stability under occlusions. It enhanced accuracy by 9.6% when compared to the use of a detection method only. This indicates that the proposed approach is a significant improvement over prior work and has the potential to effectively monitor the location of construction workers.

Published
2023
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18. Bridge-surface panoramic-image generation for automated bridge-inspection using deepmatching.

Author

Won, Jongbin, Park, Jong-Woong, Shim, Changsu, and Park, Man-Woo

Subjects
IMAGE registration, INSPECTION & review, BRIDGES, STRUCTURAL health monitoring, PARAMETRIC modeling, CONCRETE bridges, THEMATIC mapper satellite
Abstract

Visual inspection is important for the efficient maintenance of bridge structures and has recently been supplemented with the use of image-processing techniques that can localize and quantify damages using images captured from bridges. A series of overlapping bridge images can be combined for constructing a panoramic bridge-surface image in which the locations and sizes of the damages can be noted. Despite the excellent performance of image-processing techniques, generating panoramic images from a series of bridge-surface images is challenging as bridge-surface images may not possess distinct patterns or patterns that can act as reference feature points for stitching adjacent images. To address this issue, this paper presents a general method for stitching bridge-surface images using Deepmatching, which determines a pixel-wise correspondence between an image pair in comparison with conventional feature-wise matching methods. To employ Deepmatching for panoramic-image generation, (1) image matching pair search using 2D Delaunay triangulation, (2) parametric model for optimal image stitching were developed, and (3) field validation was conducted in this study. First, possible image matching pairs are organized using the two-dimensional Delaunay triangulation, and then Deepmatching is used to determine the matching points between possible image pairs. The developed parametric model refines the valid image matching pair, which is used for obtaining optimal global homographies for panoramic-image generation. For the validation of the proposed method, a lab-scale experiment on a flat concrete wall and a field experiment on a concrete bridge were conducted. The experimental validation demonstrates that the proposed method successfully identifies dense matching points between image pairs and generates a panoramic image while minimizing the occurrence of ghosting and drift. [ABSTRACT FROM AUTHOR]

Published
2021
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22. Automated monitoring of hardhats wearing for onsite safety enhancement

Author

Zhu, Zhenhua, Park, Man-Woo, and Elsafty, Nehad

Abstract

Construction is one of the most dangerous job sectors over the world. Any accidents that happen on the construction sites will bring the sufferings to the workers and their families and incur the delays and costs to the projects. Therefore, it is necessary for the contractors to monitor potential site safety issued and comply with existing safety regulations all the time. One of fundamental safety regulations is hardhat wearing. The wearing of the hardhats is always mandated and should not be violated anytime on the construction sites. In this paper, a novel method is proposed to facilitate the monitoring of whether any persons on the construction sites are wearing hardhats as required by the safety regulations. The method is built upon computer vision techniques. Under the method, human bodies and hardhats are first detected in the video frames captured by real-time on-site construction cameras. Then, their pair-wise matching is found. For those persons without the matching of the hardhats, they are identified as not wearing hardhats. The proposed method has been tested on real site videos. The test results showed that multiple persons could be monitored even if they are not wearing any real-time location sensors or tags. The test results demonstrate the potential of using live streaming or time-lapse construction site videos to facilitate the safety monitoring work on construction sites.

Published
2015
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38. 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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39. 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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40. 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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41. 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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42. 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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