Salient Traffic Sign Recognition Based on Sparse Representation of Visual Perception

摘要：

　　This paper proposes a new approach to recognize salient traffic signs,which is based on sparse representation of visual perception via visual saliency and speeded up robust features (SURF) algorithm.The proposed algorithm deals with two tasks: traffic signs detection and traffic signs recognition.Firstly,multi-scale phase spectrum of quaternion Fourier transformation method is used to obtain the location of traffic signs in scenes image.Secondly,traffic signs local sparse features are extracted by the improved algorithm based on SURF descriptors and locality-constrained linear coding (LLC) method.Finally,linear support vector machine (SVM) is used to train classifier and test recognition accuracy rate of ban traffic signs.Extensive experiments on 1000 images show that our approach can improve recognition accuracy rate and reduce running time.

关键词： Traffic sign detection Traffic sign recogntion Visual saliency Quaternion Fourier transform Sparse coding Support Vector Machine (SVM)

作者: Ce Li Lihua Tian

作者单位: College of Electrical and Information Engineering,Lanzhou University of Technology, Lanzhou, China Institute of Artificial Intelligence and Robotics Xian Jiao tong University, Xian, China

会议类型: 国际会议

会议名称: 2012年遥感中的计算机视觉国际会议

会议地点: 厦门

会议语种:英文

页码: 1-6

在线出版日期: 2012-12-16（万方平台首次上网日期，不代表论文的发表时间）