01590307

Component

Aiming to provide information of vehicle types for traffic control and management, an efficient Combined Feature (CF) extraction approach from Pyramid Histogram of Oriented Gradients (PHOG) and Curvelet Transform (CT) is proposed for description of vehicle front images, and Random Subspace Ensemble (RSE) of Linear Perception (LP) classifiers as the base classifier is then exploited for classification of thirteen classes of vehicles, i.e., Audi, Wulin, Chery, Chevrolet, Cityroen, Ford, Changan, Hyudai, Mzada, Nissan, Peugot, Buick, and Toyotas. With features extracted by CF and RSE of LP classifiers on SEU vehicle front image dataset, the holdout and cross-validation experiments are created. Results of holdout and cross-validation experiments show that CF by RSE of LP classifiers outperforms other two classifiers, i. e., PHOG features by LP classifier, CT features by LP classifier. With CF and RSE of LP classifiers, the average classification accuracies of vehicles types are over 90% in both of holdout and cross-validation experiments, which shows the effectiveness of the proposed feature extraction approach and RSE of LP classifiers in developing recognition system of vehicle types. From the confusion matrices of holdout and cross-validation experiments, it is shown that the vehicle class of Mzada has the most recognition accuracy of thirteen vehicles classes, but the vehicle class of Chery is the most difficult to classify of thirteen vehicles classes.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

01584066

16-1818

English

17p

2016

Transportation Research Board 95th Annual Meeting

Digital/other

Figures; References; Tables

Automatic vehicle classification; Histograms; Image processing; Integral transforms

Feature extraction

Data and Information Technology; Highways; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2016 Paper #16-1818

TRIS, TRB, ATRI

Jan 12 2016 4:47PM