Chapter
Aug 6, 2013
Video-Based Highway Asset Recognition and 3D Localization
Authors: Vahid Balali [email protected], Mani Golparvar-Fard [email protected], and Jesus M. de la Garza [email protected]Author Affiliations
Publication: Computing in Civil Engineering (2013)
Abstract
A key element towards development of an asset management program is an efficient data collection on high-quantity, low-cost highway assets. Despite the importance, current practices of asset data collection are still manual and time-consuming. There is a need for a well-managed asset data collection that can provide usable asset inventories to Departments of Transportation (DOTs) for further analysis and condition assessment purposes. In this paper, we present a novel video-based recognition and 3D reconstruction algorithm. Our method takes an input of video streams and combines 2D recognition with 3D reconstruction algorithms. Using a new Support Vector Machine (SVM) classifier and based on the color channels at pixel level, a set of bounding boxes is initially extracted. Using a Haar-based shape recognition algorithm, the 2D candidates are further categorized based on their shape. These candidates are placed into a texture+color recognition algorithm. The benefits and limitations of the method in detection, classification, and localization of multiple types of assets are discussed in detail.
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© 2013 American Society of Civil Engineers.
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Published online: Aug 6, 2013
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PhD Student, Vecellio Construction Engineering and Management Group, Via Department of Civil and Environmental Engineering, and Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA. E-mail: [email protected]
Assistant Professor, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL. E-mail: [email protected]
Vecellio Professor, Vecellio Construction Engineering and Management Group, Via Department of Civil and Environmental Engineering, and Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA. E-mail: [email protected]
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