A rapid learning algorithm for vehicle classification

•A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a sample’s feature value with its class label.•A rapid incremental learning algorithm of AdaBoost is designed. AdaBoost is a popular method for vehicl...

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Vydané v:Information sciences Ročník 295; s. 395 - 406
Hlavní autori: Wen, Xuezhi, Shao, Ling, Xue, Yu, Fang, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 20.02.2015
Predmet:
AdaBoost
Algorithms
Classification
Haar-like features
Incremental learning
Learning
Machine learning
Real time
State of the art
Training
Vehicle classification
Vehicles
Weak classifier
Haar-like features
AdaBoost
Incremental learning
Vehicle classification
Weak classifier
ISSN:0020-0255
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Popis
Shrnutí:•A fast learning algorithm is introduced for real-time vehicle classification.•A fast feature selection method for AdaBoost is presented by combining a sample’s feature value with its class label.•A rapid incremental learning algorithm of AdaBoost is designed. AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32×32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle’s appearance. Then, a fast training approach for the weak classifier is presented by combining a sample’s feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0020-0255
DOI:10.1016/j.ins.2014.10.040