Computer Vision-Based Intelligent Monitoring of Disruptions due to Construction Machinery Arrival Delay

Abstract

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 F1 score (IDF1), 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.