Traffic Sign Recognition Using Perturbation Method

摘要：

　　Automatic traffic sign recognition (TSR) expects high accuracy and speed for real-time applications in intelligent transportation systems.Convolutional neural networks (CNNs) have yielded state-of-the-art performance on the public dataset GTSRB,but involve intensive computation.In this paper,we propose a traffic sign recognition method using computationally efficient feature extraction and classification techniques,and using the perturbation strategy to improve the accuracy.On the GTSRB dataset,using gradient direction histogram feature and learning vector quantization (LVQ) classifier achieves a test accuracy 98.48%.Using simple perturbation operations of image translation,the accuracy is improved to 98.88%.The accuracy is higher than that of single CNN and the speed is much higher.

关键词： Traffic sign recognition classification perturbation

作者: Linlin Huang Fei Yin

作者单位: School of Electronics and Information Engineering,Beijing Jiaotong University No.3 Shangyuancun,Haid National Laboratory of Pattern Recognition (NLPR) Institute of Automation of Chinese Academy of Scie

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 518-527

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）