Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature
AbstractRobust 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Yin, S.; Ouyang, P.; Liu, L.; Guo, Y.; Wei, S. Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature. Sensors 2015, 15, 2161-2180.
Yin S, Ouyang P, Liu L, Guo Y, Wei S. Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature. Sensors. 2015; 15(1):2161-2180.Chicago/Turabian Style
Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun. 2015. "Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature." Sensors 15, no. 1: 2161-2180.