Vehicle Brand Classification Method Based on PCA-NET Under Complex Background

Abstract

At present, the classification of vehicle brands as an important unit in the urban intelligent transportation system has become a hot spot for researchers from various countries. The classification and recognition of videos and images have been effectively researched and applied. In order to achieve better classification effect and higher recognition efficiency, this paper uses Principal Component Analysis-Net (PCA-NET) to realize the classification of vehicle brand, and combined with Support Vector Machines (SVM) to achieve. From the experimental results, this method can effectively extract the vehicle front view and achieve classification. The classification accuracy is high, which can reach 93.2%. In addition, this method has flexible adaptability to complex background conditions.