DocumentCode :
1363337
Title :
Vehicle Identification Via Sparse Representation
Author :
Wang, Shuang ; Cui, Lijuan ; Liu, Dianchao ; Huck, Robert ; Verma, Pramode ; Sluss, James J. ; Cheng, Samuel
Author_Institution :
Sch. of Electr. & Comput. Eng., Univ. of Oklahoma, Tulsa, OK, USA
Volume :
13
Issue :
2
fYear :
2012
fDate :
6/1/2012 12:00:00 AM
Firstpage :
955
Lastpage :
962
Abstract :
In this paper, we propose a system using video cameras to perform vehicle identification. We tackle this problem by reconstructing an input by using multiple linear regression models and compressed sensing, which provide new ways to deal with three crucial issues in vehicle identification, namely, feature extraction, online vehicle identification database buildup , and robustness to occlusions and misalignment. The results show the capability of the proposed approach.
Keywords :
feature extraction; regression analysis; road vehicles; traffic engineering computing; video cameras; video signal processing; visual databases; compressed sensing; feature extraction; misalignment robustness; multiple linear regression models; occlusion robustness; online vehicle identification database buildup; sparse representation; video cameras; Cameras; Databases; Feature extraction; Monitoring; Training; Vectors; Vehicles; Sparse representation; vehicle identification;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2011.2171034
Filename :
6062414
Link To Document :
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