License plate detection algorithm based on gentle AdaBoost algorithm with a cascade structure

Authors:
Dong Cui;Dongbing Gu;Hua Cai;Junxi Sun
Affiliations:
School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China;School of Compute Science and Electronic Engineering, University of Essex, Colchester, UK;School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China;School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

Citing 4
Cited 0

Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Color Texture-Based Object Detection: An Application to License Plate Localization

SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Learning-Based License Plate Detection Using Global and Local Features

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
The Application of a Convolution Neural Network on Face and License Plate Detection

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper aims at automatically detection of car license plates via image processing techniques. The method used is a so-called gentle AdaBoost algorithm which is combined with a cascade structure. The gentle AdaBoost (GAB) algorithm is known to have a higher detection rate and a lower false positive rate than the basic discrete AdaBoost (DAB) which is currently reported being used for the license plate detection. The use of a cascade structure in the gentle AdaBoost algorithm saves the computation time. Several testing results are provided and the comparisons with other algorithms are made.