Real-time smart video surveillance to manage safety: A case study of a transport mega-project

Abstract

There is a tendency for accidents and even fatalities to arise when people enter hazardous work areas during the construction of projects in urban areas. A limited amount of research has been devoted to developing vision-based proximity warning systems that can determine when people enter a hazardous area automatically. Such systems, however, are unable to identify specific hazards and the status of a piece of plant (e.g., excavator) in real-time. In this paper, we address this limitation and develop a real-time smart video surveillance system that can detect people and the status of plant (i.e. moving or stationary) in a hazardous area. The application of this approach is demonstrated during the construction of a mega-project, the Wuhan Rail Transit System in China. We reveal that our combination of computer vision and deep learning can accurately recognize people in a hazardous work area in real-time during the construction of transport projects. Our developed systems can provide instant feedback concerning unsafe behavior and thus enable appropriate actions to be put in place to prevent their re-occurrence.