Vision-based motion prediction for construction workers safety in real-time multi-camera system

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuntae Jeon , Dai Quoc Tran , Almo Senja Kulinan , Taeheon Kim , Minsoo Park , Seunghee Park
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Abstract

Ensuring worker safety on dynamic construction sites is a significant challenge, especially as it is crucial to immediately identify potential hazards and warn workers. Existing computer vision-based motion prediction methods often overlook the false negative issue caused by the noisy environments of construction sites, and treat tracking and trajectory prediction as disconnected processes. This study introduces MPSORT, a method that suggests trajectory prediction-based tracking with trajectory interpolation for vision-based automated safety monitoring. The proposed method predicts the future movements of construction workers and vehicles using multiple CCTV cameras, and localizes these predictions onto the construction site’s bird’s eye view (BEV) map. This enables to send the real-time warnings to workers in danger, preventing accidents such as collision, fall, and getting stuck. We evaluated the performance of our method in both object tracking and trajectory prediction tasks on dataset from multiple CCTV cameras on construction sites. The object tracking results show an approximate 10% increase in the number of tracked objects, and the trajectory prediction results indicate an ADE of 7.138 and an FDE of 12.493, reflecting improvements of more than 5% and 2% in ADE and FDE, respectively, compared to previous methods. Overall, these findings are significant for minimizing accidents and enhancing safety and efficiency on construction sites.
基于视觉的运动预测,在实时多摄像头系统中保障建筑工人安全
确保动态建筑工地的工人安全是一项重大挑战,尤其是立即识别潜在危险并向工人发出警告至关重要。现有的基于计算机视觉的运动预测方法往往忽略了建筑工地嘈杂环境所造成的假负问题,并将跟踪和轨迹预测视为互不关联的过程。本研究介绍的 MPSORT 是一种基于轨迹预测的跟踪方法,建议将轨迹插值用于基于视觉的自动安全监控。所提出的方法利用多个 CCTV 摄像机预测建筑工人和车辆的未来移动,并将这些预测定位到建筑工地的鸟瞰图(BEV)上。这样就能向处于危险中的工人发出实时警告,防止发生碰撞、坠落和卡住等事故。我们在建筑工地多个闭路电视摄像机的数据集上评估了我们的方法在物体跟踪和轨迹预测任务中的性能。物体跟踪结果表明,被跟踪物体的数量增加了约 10%;轨迹预测结果表明,ADE 为 7.138,FDE 为 12.493,与以前的方法相比,ADE 和 FDE 分别提高了 5% 和 2%。总体而言,这些研究结果对于最大限度地减少事故、提高建筑工地的安全和效率具有重要意义。
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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