Smart vision-based monitoring system for heavy construction and surface mining jobsites

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Construction operations, like other industrial and service processes, require real-time feedback systems to measure key performance indicators, such as productivity, and to take steps to correct problems that interfere with efficient operations. Currently, automated systems are broadly used in many industries and services and have improved productivity substantially. However, the construction industry has been slow to adopt automation. To develop effective automated feedback systems, researchers have tested different sensing technologies to monitor workers and equipment in the uniquely rugged environment of construction jobsites. Earthmoving projects, such as highway construction and surface mining operations, have been a primary initial focus in the industry for developing automated sensing systems. However, monitoring different types of equipment in a dynamic environment is a challenge; systems are challenged to understand the context of a construction site. For example, hydraulic excavators can take any number of shapes, which strains the ability of software to classify the actions of the equipment. Low-cost digital cameras, combined with promising advances in computer vision, make vision-based systems likely candidates for solutions in construction operations. The current systems using vision-based algorithms to monitor heavy operations, however, aren’t ideal and require some level of human intervention to operate successfully. For instance, they are not able to understand the scene and the user must determine the action type to be monitored; also, the user has to set the viewfinder of the camera on the operation of interest. The objective of this research program is to bridge the gap between theoretical computer vision and machine learning algorithms on one hand, and practical applications on the other, to develop a generic smart vision-based system to measure the productivity of earthmoving processes in construction and surface mining jobsites. Beyond object recognition and tracking, the proposed research program will investigate development of algorithms for action recognition and understanding the scene based on identified actions, automated control of panning and zooming features of a camera, proactive control of a network of cameras (including the ability to transfer tracking among cameras in the network), productivity estimation, deviation detection, and integration of developed modules. The final result will be an automated framework capable of tracking different kinds of machinery in different types of construction environments.