Title: Effective traffic signs recognition via kernel PCA network

Authors: Jianming Zhang; Qianqian Huang; Honglin Wu; Yangchun Liu

Addresses: Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan Province, China ' Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan Province, China ' Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan Province, China ' Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan Province, China

Abstract: The classification of traffic sign images is easily affected by the change of weather, camera angles, occlusion, etc. The traditional image recognition methods not only require high image quality, but also need to find effective features manually. However, the convolutional neural networks can automatically extract high-level, abstract features which are robust to the variations. This paper presents a novel and effective traffic signs recognition approach via the kernel PCA network based on convolutional neural networks. The kernel PCA network uses two-layer convolutional network to extract abstract features, and convolution kernels in each layer are directly calculated by the kernel principal component analysis. After nonlinear mapping and pooling, support vector machines are applied to the final classification. The approach can achieve a high recognition rate on the German traffic signs recognition benchmark dataset containing reliable ground-truth data.

Keywords: kernel PCA network; kernel principal component analysis; KPCA; German traffic signs recognition benchmark; GTSRB.

DOI: 10.1504/IJES.2018.090568

International Journal of Embedded Systems, 2018 Vol.10 No.2, pp.120 - 125

Received: 23 Jun 2016
Accepted: 09 Aug 2016
Published online: 22 Mar 2018 *

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