A rapid learning algorithm for vehicle classification

Wen, Xuezhi, Shao, Ling, Xue, Yu and Fang, Wei (2015) A rapid learning algorithm for vehicle classification. Information Sciences, 295. pp. 395-406. ISSN 0020-0255

Full text not available from this repository.

Abstract

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 x 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.

Item Type:	Article
Uncontrolled Keywords:	AdaBoost; Weak classifier; Haar-like features; Incremental learning; Vehicle classification
Subjects:	G600 Software Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	15 Jan 2015 09:21
Last Modified:	10 Oct 2019 15:16
URI:	http://nrl.northumbria.ac.uk/id/eprint/21167

Downloads