title: Fast traffic sign recognition with a rotation invariant binary pattern based feature.
abstract: Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.
authors.affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. yinsy@tsinghua.edu.cn.
authors.forename: Shouyi
authors.initials: S
authors.lastname: Yin
authors.affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. oyangpeng12@163.com.
authors.forename: Peng
authors.initials: P
authors.lastname: Ouyang
authors.affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. liulb@tsinghua.edu.cn.
authors.forename: Leibo
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Computing, Imperial College, London SW7 2AZ, UK. y.guo@imperial.ac.uk.
authors.forename: Yike
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. wsj@tsinghua.edu.cn.
authors.forename: Shaojun
authors.initials: S
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated