Abstract: To increase the speed and accuracy of vehicle recognition, an improved hierarchical Histogram of Oriented Gradient (HOG) symmetry algorithm was proposed. First, the HOG features are improved to get the hierarchical HOG symmetry features which are fused with Local Binary Pattern (LPB) features to get the fusion features. Second, the fusion features are taken as the training sample of Support Vector Machine (SVM) classifier. Meanwhile, the Principal Component Analysis (PCA) is used to reduce the dimensions for decreasing the complexity of the classifier. Finally, the SVM is used to recognize the appearance features of the vehicle. Simulation results show that the proposed vehicle type recognition algorithm can not only increase the feature extraction speed but also improve the detection accuracy, enhancing the real-time recognition of original vehicle images. The mean processing speed is about 26.2 frames/s and the accuracy is about 94.58%, increasing by 7.98% in comparison to traditional HOG feature extraction algorithm. The method can effectively increase the accuracy of vehicle recognition and also reduce the computing time caused by high dimensional features.

Key words: computer application, vehicle type recognition, HOG feature, LBP feature, feature extraction, principal component analysis(PCA), support vector machine(SVM)