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