TY - JOUR
T1 - A rapid learning algorithm for vehicle classification
AU - Wen, Xuezhi
AU - Shao, Ling
AU - Xue, Yu
AU - Fang, Wei
PY - 2015/2/20
Y1 - 2015/2/20
N2 - 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.
AB - 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.
KW - AdaBoost
KW - Weak classifier
KW - Haar-like features
KW - Incremental learning
KW - Vehicle classification
U2 - 10.1016/j.ins.2014.10.040
DO - 10.1016/j.ins.2014.10.040
M3 - Article
VL - 295
SP - 395
EP - 406
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -