Integrating Spatial Proximity and Visual Feature Similarity for Crew Group Detection in Construction Site
Journal
Proceedings of the International Symposium on Automation and Robotics in Construction
Start Page
1121
End Page
1128
ISSN
24135844
ISBN (of the container)
9780645832228
ISBN
9780645832228
Date Issued
2025
Author(s)
Abstract
Crew-level productivity analysis plays a crucial role in construction site management, as it provides a macro-level understanding of workforce performance. While traditional productivity measurement methods, such as work sampling, group timing technique, and five-minute rating, offer valuable insights, they rely heavily on manual observation, making them labor-intensive and prone to inaccuracy. In this study, we propose a deep learning-based framework for automated crew-level identification. The framework employs a graph-based approach that integrates visual feature similarity and spatial proximity of workers, combined with clustering algorithms, to detect and analyze worker groups. The proposed method is validated on a construction site dataset collected from a rebar installation task. Experimental results demonstrate the framework’s effectiveness, achieving high accuracy in group detection with robust performance across various evaluation metrics. This work highlights the potential of automated systems to enhance construction site management by reducing reliance on manual observation and providing real-time insights into crew-level productivity.
Event(s)
42nd International Symposium on Automation and Robotics in Construction, ISARC 2025, Montreal, 28 July 2025 - 31 July 2025
Subjects
Clustering
Group Detection
Human Activity Recognition
Image Understanding
Publisher
International Association for Automation and Robotics in Construction (IAARC)
Type
conference paper