TY - GEN
T1 - Scalable non-parametric parsing for segmentation and recognition of high-quantity, low-cost highway assets from car-mounted video streams
AU - Balali, Vahid
AU - Golparvar-Fard, Mani
PY - 2014
Y1 - 2014
N2 - Systematic condition assessment on high-quantity low-cost highway assets requires frequent reporting on location and up-to-date status of these assets. Recent research on video-based assessment of assets have focused primarily on detecting traffic signs during data collection and are not applicable to other assets, such as guardrails and light poles. To overcome such limitations, this paper presents fast graph-based segmentation and super-parsing algorithms, which efficiently segment highway assets from 2D video streams. Using a fast graph-based segmentation algorithm, superpixels are obtained from each frame, and their appearance is computed using a histogram of textons and dense SIFT-descriptors. A likelihood ratio score is obtained for each superpixel and an asset label that maximizes the ratio is assigned. Given a frame to be interpreted, the algorithm performs global matching against the training set, followed by superpixel-level matching and efficient Markov Random Field (MRF) optimization. The MRF simultaneously labels video frame regions into semantic and geometric classes of assets. Experimental results are presented on the Virginia Tech Smart Road research facility on a 2.2 mile highway. The work contributes to the body of knowledge by detecting 3D assets that previously have not been detectable by state-of-the-art methods. It also enables further development of techniques that can recognize subcategories of highway assets.
AB - Systematic condition assessment on high-quantity low-cost highway assets requires frequent reporting on location and up-to-date status of these assets. Recent research on video-based assessment of assets have focused primarily on detecting traffic signs during data collection and are not applicable to other assets, such as guardrails and light poles. To overcome such limitations, this paper presents fast graph-based segmentation and super-parsing algorithms, which efficiently segment highway assets from 2D video streams. Using a fast graph-based segmentation algorithm, superpixels are obtained from each frame, and their appearance is computed using a histogram of textons and dense SIFT-descriptors. A likelihood ratio score is obtained for each superpixel and an asset label that maximizes the ratio is assigned. Given a frame to be interpreted, the algorithm performs global matching against the training set, followed by superpixel-level matching and efficient Markov Random Field (MRF) optimization. The MRF simultaneously labels video frame regions into semantic and geometric classes of assets. Experimental results are presented on the Virginia Tech Smart Road research facility on a 2.2 mile highway. The work contributes to the body of knowledge by detecting 3D assets that previously have not been detectable by state-of-the-art methods. It also enables further development of techniques that can recognize subcategories of highway assets.
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U2 - 10.1061/9780784413517.0013
DO - 10.1061/9780784413517.0013
M3 - Conference contribution
AN - SCOPUS:84904614336
SN - 9780784413517
T3 - Construction Research Congress 2014: Construction in a Global Network - Proceedings of the 2014 Construction Research Congress
SP - 120
EP - 129
BT - Construction Research Congress 2014
PB - American Society of Civil Engineers
T2 - 2014 Construction Research Congress: Construction in a Global Network, CRC 2014
Y2 - 19 May 2014 through 21 May 2014
ER -