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Tracking Construction Site Resources with Machine Vision

Resource tracking is a vital aspect of construction project management and control systems. Tracking technologies based on radio frequency (RF) communications have dominated the market and have proven reliable and adequate in many typical construction environments. These RF technologies rely on sensors (tags) placed on each resource that are “read” by remote (satellites) or local reader devices. In small construction sites the labor overhead generated from the deployment, maintenance and decommissioning of such tags and readers is small. However in medium and large-scale sites, where tracking is needed for thousands of materials and hundreds of personnel and equipment, the labor overhead of RF technologies reaches prohibitive costs when compared to the anticipated benefits. As a result, automated tracking solutions have so far been limited to small projects and case studies that cover only small segments of larger projects.

“Remote” tracking, defined here as tracking resources from a distance without the need for tags attached on them has the potential to reduce this cost down to feasible levels. Vision tracking is the most popular and inexpensive form of remote tracking. It finds the location of the tracked object in each frame by comparing with its position on the previous frame through a process known as image alignment. However, the state-of-the-art vision tracking technologies do not have the ability to track resources automatically and in 3D. Yet.

CEE Assistant Professor Ioannis Brilakis and doctoral student Man Woo Park, with support from the U.S. National Science Foundation (Grant # 0933931), are validating the ability of a novel framework to track multiple resources simultaneously from stationary construction cameras. This framework starts by recognizing most common project related resources (materials, personnel and equipment) from each video, matching them across all views, and using the result to initialize all independent tracking sequences. This way in each subsequent frame, the 2D positions of the matched entities can be used to calculate its location in space. The NSF-funded project aims at calculating the performance of each step based on established metrics and comparing it to the performance of RF technologies. If comparable or better performance is determined, the cost and time savings associated with the elimination of tags on each entity and its replacement by a drastically smaller number of cameras will make this work a breakthrough for the construction industry that will change the direction of project monitoring and control technologies.

Vision Tracking Framework (Click for larger image)

Dr. Ioannis Brilakis joined CEE in 2009 and established the Construction Information Technology (CIT) group. The CIT group focuses on creating, synthesizing, modifying and/or adapting the next generation IT methods and systems needed to automate and improve construction, inspection and rehabilitation methods. Brilakis’ primary research interests lie at the intersection of machine vision and construction engineering. The long term goal of his research team is to create the tools needed to virtualize real world structures and construction processes by automating the opposite of the design-construction process; digitizing the result of construction into a virtual 3D model.

For more information about this initiative, please visit: http://www.cit.gatech.edu/