A rapid learning algorithm for vehicle classification

Xuezhi Wen, Ling Shao, Yu Xue, Wei Fang

Research output: Contribution to journalArticlepeer-review

446 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)395-406
JournalInformation Sciences
Early online date23 Oct 2014
Publication statusPublished - 20 Feb 2015


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