Automated Vision-Based Sensing for Site Operations Analysis

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This research seeks to prove that it is possible to reliably and automatically track work progress and multiple resources with images (video and/or time-lapse) in order to reproduce the daily workflow activities associated to a construction worksite. The task of measuring the progress of construction site activities that involve workers, large machines, and materials, has often been a subjective and intensive manual process that is prone to error and, in real operations, frequently out-of-date. Demonstrating that an active vision system can effectively analyze and assess work-site progress will assist project managers by reducing the time spent monitoring and interpreting project status and performance, thus enabling increased attention to control of cost and schedule. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the industry are envisioned. The track data will be interpreted and used to provide understanding of the spatio-temporal evolution of a worksite for automatically generating knowledge about worksite operations. In an information-based framework, much effort is spent acquiring and interpreting information. In a knowledge-based framework, efforts are allocated to making decisions based on the interpreted information.If successful, this research will transform the review and management of construction operations from being information-based to knowledge-based, thus saving human resources and improving decision effectiveness. This research has broader appeal beyond construction. Research domains incorporating or requiring vision-based sensing, diverse resources (people, small to heavy machinery, goods, etc.), and processing of the visual data for awareness of operations and activities are additional investigation domains. Examples include airport ground operations and mining operations. Contributions are also expected in the fields of machine learning and computer vision. The proposed research will impact research into site operations by enabling the automated monitoring and tracking of site resources. Video-based monitoring and processing algorithms provide a non-intrusive, easy, and, rapid mechanism for generating a body of operational information and knowledge which, when made available, will enable inquiry into construction operations that is currently not possible. Longer term, this research will serve as a valuable aid to project management by enabling tighter control and greater efficiency. By making project management and workforce more aware of the performance status of their project and their work environment, potential savings to the construction and other industries are envisioned. This research will also actively include and drive the education of the next generation of engineers (civil, electrical, and computational engineering) and construction labor pool. The research has a dedicated outreach plan to involve in this research a broad spectrum of students from high schools and industry professionals who are interested in advanced hard- and software technology