Wei Sun1,3,*, Hongji Du1, Shoubai Nie2,3, Xiaozheng He4
CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 147-161, 2019, DOI:10.32604/cmc.2019.03581
Abstract Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic… More >
Login
Register
