Recognition of Car Makes and Models From a Single Traffic-Camera Image

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and co...

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Bibliographic Details
Published inIEEE transactions on intelligent transportation systems Vol. 16; no. 6; pp. 3182 - 3192
Main Authors He, Hongsheng, Shao, Zhenzhou, Tan, Jindong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Cameras
Feature extraction
intelligent transportation
Lighting
Neural networks
traffic surveillance
Vehicle identification
vehicle tracking
Vehicles
intelligent transportation
Vehicle identification
traffic surveillance
vehicle tracking
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Summary:This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2015.2437998