Paper Info
Title
Learning-Based License Plate Detection Using Global and Local Features
Abstract
This paper proposes a license plate detection algorithm using both global statistical features and local Haar-like features. Classifiers using global statistical features are constructed firstly through simple learning procedures. Using these classifiers, more than 70% of background area can be excluded from further training or detecting. Then the AdaBoost learning algorithm is used to build up the other classifiers based on selected local Haar-like features. Combining the classifiers using the global features and the local features, we obtain a cascade classifier. The classifiers based on global features decrease the complexity of the system. They are followed by the classifiers based on local Haar-like features, which makes the final classifier invariant to the brightness, color, size and position of license plates. The encouraging detection rate is achieved in the experiments
Year
DOI
Venue
2006
10.1109/ICPR.2006.758
ICPR (2)
Keywords
Field
DocType
local haar-like feature,local features,cascade classifier,statistics,selected local haar-like feature,learning (artificial intelligence),license plate detection algorithm,global statistical feature,local haar-like features,encouraging detection rate,global feature,image classification,license plate,object detection,learning-based license plate detection,final classifier invariant,local feature,adaboost learning algorithm,learning artificial intelligence
Computer science,Artificial intelligence,Classifier (linguistics),Contextual image classification,License,Object detection,Computer vision,AdaBoost,Pattern recognition,Object-class detection,Cascading classifiers,Invariant (mathematics),Machine learning
Conference
Volume
ISSN
ISBN
2
1051-4651
0-7695-2521-0
Citations 
PageRank 
References 
52
2.38
9
Authors
4
Name
Order
Citations
PageRank
Huaifeng Zhang124018.84
Wenjing Jia232545.08
Xiangjian He3932132.03
Qiang Wu453454.06