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Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay
https://orcid.org/0000-0002-1675-2368
Construction disruptions often cause schedule delays and budget overruns. Accurate disruption monitoring is crucial for the timely recovery of affected construction projects. This study proposes a computer vision-based (CVB) multiobject tracking (MOT) method for disruption monitoring in complex construction environments. This approach incorporates a sparse-optical-flow-based module for short-term undetected mask estimation and a deep re-identification (ReID) module for long-term occlusion handling. We also build a large-scale dataset containing 100 construction videos and 155,774 annotations to train the proposed MOT method. The experimental results show that our method outperforms state-of-the-art trackers across multiple representative evaluation metrics: the higher order tracking accuracy (HOTA), detection accuracy (DetA), association accuracy (AssA), localization accuracy (LocA), identification score (), and identity switches (IDSW) are 61.6%, 57.9%, 66.4%, 91.1%, 64.0%, and 133, respectively. Additionally, field tests confirm the effectiveness of the MOT method in multiple truck tracking, arrival time recording, and disruption monitoring at construction sites.
Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay
https://orcid.org/0000-0002-1675-2368
Construction disruptions often cause schedule delays and budget overruns. Accurate disruption monitoring is crucial for the timely recovery of affected construction projects. This study proposes a computer vision-based (CVB) multiobject tracking (MOT) method for disruption monitoring in complex construction environments. This approach incorporates a sparse-optical-flow-based module for short-term undetected mask estimation and a deep re-identification (ReID) module for long-term occlusion handling. We also build a large-scale dataset containing 100 construction videos and 155,774 annotations to train the proposed MOT method. The experimental results show that our method outperforms state-of-the-art trackers across multiple representative evaluation metrics: the higher order tracking accuracy (HOTA), detection accuracy (DetA), association accuracy (AssA), localization accuracy (LocA), identification score (), and identity switches (IDSW) are 61.6%, 57.9%, 66.4%, 91.1%, 64.0%, and 133, respectively. Additionally, field tests confirm the effectiveness of the MOT method in multiple truck tracking, arrival time recording, and disruption monitoring at construction sites.
Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay
https://orcid.org/0000-0002-1675-2368
Construction disruptions often cause schedule delays and budget overruns. Accurate disruption monitoring is crucial for the timely recovery of affected construction projects. This study proposes a computer vision-based (CVB) multiobject tracking (MOT) method for disruption monitoring in complex construction environments. This approach incorporates a sparse-optical-flow-based module for short-term undetected mask estimation and a deep re-identification (ReID) module for long-term occlusion handling. We also build a large-scale dataset containing 100 construction videos and 155,774 annotations to train the proposed MOT method. The experimental results show that our method outperforms state-of-the-art trackers across multiple representative evaluation metrics: the higher order tracking accuracy (HOTA), detection accuracy (DetA), association accuracy (AssA), localization accuracy (LocA), identification score (), and identity switches (IDSW) are 61.6%, 57.9%, 66.4%, 91.1%, 64.0%, and 133, respectively. Additionally, field tests confirm the effectiveness of the MOT method in multiple truck tracking, arrival time recording, and disruption monitoring at construction sites.
Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay
J. Comput. Civ. Eng.
Yan, Xuzhong (author) / Jin, Rui (author) / Zhang, Hong (author) / Gao, Hui (author) / Xu, Shuyuan (author)
2025-05-01
Article (Journal)
Electronic Resource
English
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