AI-Powered Surveillance for Intelligent Construction Site Management

The construction industry grapples with significant inefficiencies in equipment management, leading to project delays and cost overruns. Traditional manual monitoring methods are labor-intensive, prone to error, and fail to provide comprehensive coverage of large sites. This paper explores a practical AI-driven surveillance system that automates the tracking of construction equipment in real time. By leveraging advanced object detection models enhanced with a novel clustering technique, the system reliably identifies machinery even when small or partially obscured—a common challenge on active sites. This automation provides project managers with unprecedented visibility into asset utilization, enabling data-driven decisions to optimize resource allocation, reduce idle time, and improve operational efficiency. The proposed system represents a significant step towards intelligent construction site management, transforming raw video feeds into actionable business intelligence.