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Continuous multi-target tracking across disjoint camera views for field transport productivity analysis
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.autcon.2025.105984
Xiaoling Wang, Dongze Li, Jiajun Wang, Dawei Tong, Ruiqi Zhao, Zhongzhen Ma, Jiandong Li, Benyang Song

Field transport productivity analysis is crucial for scheduling large-scale earth–rock works. Although camera surveillance facilitates the monitoring of transportation activities, disjoint views from sparse cameras result in discontinuous monitoring. To address this issue, a single-camera tracking with cascade R-CNN is used for target detection, and an improved TransReID for appearance feature extraction. Subsequently, these features were utilized by the WDA-Tracker algorithm to associate targets across discontinuous camera views. Enhancements in the improved TransReID include multi-scale information extraction, and the logic information building module mitigates the substantial scene variation between nonadjacent cameras, which affects the consistency of the target appearance. The dataset incorporating self-made truck images had a Rank-1 accuracy of 90.1 %, outperforming the original TransReID accuracy of 86.2 %, and experiments at a hydropower site with three cameras showed a multi-camera tracking accuracy of 98.98 %. This method accurately calculates transport productivity and provides information on construction progress and dispatch plans.

中文翻译:


跨不相交的相机视图进行连续多目标跟踪,以进行现场运输生产率分析



野外运输生产率分析对于安排大规模土石工程至关重要。尽管摄像机监控有助于监控运输活动,但来自稀疏摄像机的不相交视图会导致不连续的监控。为了解决这个问题,使用级联 R-CNN 的单相机跟踪进行目标检测,并使用改进的 TransReID 进行外观特征提取。随后,WDA-Tracker 算法利用这些功能来关联不连续摄像机视图中的目标。改进的 TransReID 中的增强功能包括多尺度信息提取,而逻辑信息构建模块减轻了非相邻相机之间的大量场景变化,这会影响目标外观的一致性。包含自制卡车图像的数据集的 Rank-1 准确率为 90.1 %,优于原始 TransReID 准确率 86.2 %,在具有三个摄像头的水电站现场进行的实验显示多摄像头跟踪准确率为 98.98 %。这种方法可以准确计算运输生产率,并提供有关施工进度和调度计划的信息。
更新日期:2025-01-21
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