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OTCREOS9.1-16-F 1

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Commercial vehicle route tracking using video detection

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Okla State Agency	Transportation Center, Oklahoma
Okla Agency Code	'OKT'
Title	Commercial vehicle route tracking using video detection
Authors	Cheng, Samuel. Huck, Robert C. Verma, Pramode. Ghosh, Anjan. Sluss, James J. (James Jackson) University of Oklahoma. Oklahoma Transportation Center.
Publisher	Oklahoma Transportation Center
Publication Date	2010-10
Publication type	Research Report/Study
Subject	Commercial vehicles. Intelligent transportation systems. Motor vehicles--Automatic location systems. Commercial vehicle operations. Tracking systems.
Purpose	Interstate commercial vehicle traffic is a major factor in the life of any road surface. The ability to track these vehicles and their routes through the state can provide valuable information to planning activities. We propose a method using video cameras to capture critical information about commercial vehicles when they enter the state and store this information for later retrieval to provide tracking functions. As these vehicles continue on their routes, additional cameras will capture images that can be used for route tracking. By using these data, reports and highway utilization maps could be generated showing commercial vehicle routes and vehicle counts for state highways. Spurred by the competitive performance potential realized in face recognition via sparse representation, we treat the problem of vehicle identification with different video sources as signal reconstruction out of multiple linear regression models and use compressive sensing to solve this problem. By employing a Bayesian formalism to compute the l-1 minimization of the sparse weights, the proposed framework provides new ways to deal with three crucial issues in vehicle identification: feature extraction, online vehicle identification dataset build up, and robustness to occlusions and misalignment. For feature extraction, we use the simple down-sampled features which offer good identification performance as long as the features space is sparse enough. The theory also provides a validation scheme to decide if a newly identified vehicle has been included in the dataset. Moreover, unlike PCA or other similar algorithms, using down-sampling based features, one can easily introduce features of newly identified vehicles into the vehicle identification database without manipulating the existing data in the database. Finally, Bayesian formalism provides a measure of confidence for each sparse coefficient. We have conducted experiments to include different types of vehicles on the interstate highway to verify the efficiency and accuracy of our proposed system. The results show that the proposed framework cannot only handle the route tracking of commercial vehicles, but works well for all classes of vehicles.
Notes	Final report; OTCREOS9.1-16-F
OkDocs Class#	Z2285.8 C734v 2010
Digital Format	PDF, Adobe Reader required
ODL electronic copy	Downloaded from agency website: http://www.oktc.org/otc/files/finalReports/OTCREOS9.1-16-F.pdf
Rights and Permissions	This Oklahoma state government publication is provided for educational purposes under U.S. copyright law. Other usage requires permission of copyright holders.
Language	English
Date created	2013-03-21
Date modified	2013-03-21
OCLC number	890222617

Title	OTCREOS9.1-16-F 1
Full text	ECONOMIC ENHANCEMENT THROUGH INFRASTRUCTURE STEWARDSHIP COMMERCIAL VEHICLE ROUTE TRACKING USING VIDEO DETECTION SAMUEL CHENG, PH.D. ROBERT C. HUCK PRAMODE VERMA, PH.D. ANJAN GHOSH, PH.D. JAMES SLUSS, JR., PH.D. OTCREOS9.1-16-F Oklahoma Transportation Center 2601 Liberty Parkway, Suite 110 Midwest City, Oklahoma 73110 Phone: 405.732.6580 Fax: 405.732.6586 www.oktc.org
Date created	2013-03-21
Date modified	2013-03-21

Commercial vehicle route tracking using video detection

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Commercial vehicle route tracking using video detection

Commercial vehicle route tracking using video detection