TY - GEN
T1 - A new traffic sign recognition system with IFRS detector and MP-SVM classifier
AU - Huang, Yuan Shui
AU - Fu, Meng Yin
AU - Ma, Hong Bin
PY - 2010
Y1 - 2010
N2 - The design of traffic sign recognition (TSR) system, one important subsystem of Advanced Driver Assistance System (ADAS), has been a challenge practical problem for many years due to the complex issues like road environments, lighting conditions, occlusion, and so on. In this paper, we introduce a new TSR system, whose effectiveness has been tested through extensive experiments. The established TSR system mainly consists of two parts, i.e. traffic sign detector and traffic sign classifier. In this system, the traffic sign detection is implemented with a new method based on improved fast radial symmetry detector, for detecting a class of circular prohibitive traffic signs efficiently and robustly. The traffic sign classification is accomplished through moments-based pictogram support vector machine (MP-SVM) classifer. Two kinds of features, Zernike Moments and Pseudo-Zernike Moments, are used to represent the pictogram, which will be fed to SVM for training and testing. Experiment results have validified the robust detection effects and high classification accuracy.
AB - The design of traffic sign recognition (TSR) system, one important subsystem of Advanced Driver Assistance System (ADAS), has been a challenge practical problem for many years due to the complex issues like road environments, lighting conditions, occlusion, and so on. In this paper, we introduce a new TSR system, whose effectiveness has been tested through extensive experiments. The established TSR system mainly consists of two parts, i.e. traffic sign detector and traffic sign classifier. In this system, the traffic sign detection is implemented with a new method based on improved fast radial symmetry detector, for detecting a class of circular prohibitive traffic signs efficiently and robustly. The traffic sign classification is accomplished through moments-based pictogram support vector machine (MP-SVM) classifer. Two kinds of features, Zernike Moments and Pseudo-Zernike Moments, are used to represent the pictogram, which will be fed to SVM for training and testing. Experiment results have validified the robust detection effects and high classification accuracy.
KW - Fast radial symmetry
KW - Pseudo-zernike moments
KW - Support vector machine
KW - Traffic sign recognition
UR - http://www.scopus.com/inward/record.url?scp=79952405696&partnerID=8YFLogxK
U2 - 10.1109/GCIS.2010.63
DO - 10.1109/GCIS.2010.63
M3 - Conference contribution
AN - SCOPUS:79952405696
SN - 9780769543048
T3 - Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
SP - 23
EP - 27
BT - Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
T2 - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
Y2 - 16 December 2010 through 17 December 2010
ER -