TY - GEN
T1 - A robust, coarse-to-fine traffic sign detection method
AU - Wang, Gangyi
AU - Ren, Guanghui
AU - Wu, Zhilu
AU - Zhao, Yaqin
AU - Jiang, Lihui
PY - 2013
Y1 - 2013
N2 - We present a traffic sign detection method which has won the first place for the prohibitory and mandatory signs and the third place for the danger signs in the GTSDB competition. The method uses the histogram of oriented gradient (HOG) and a coarse-to-fine sliding window scheme. Candidate ROIs are first roughly detected within a small-sized window, and then further verified within a large-sized window for higher accuracy. Experimental results show that the proposed method achieves high recall and precision ratios, and is robust to various adverse situations including bad lighting condition, partial occlusion, low quality and small projective deformation.
AB - We present a traffic sign detection method which has won the first place for the prohibitory and mandatory signs and the third place for the danger signs in the GTSDB competition. The method uses the histogram of oriented gradient (HOG) and a coarse-to-fine sliding window scheme. Candidate ROIs are first roughly detected within a small-sized window, and then further verified within a large-sized window for higher accuracy. Experimental results show that the proposed method achieves high recall and precision ratios, and is robust to various adverse situations including bad lighting condition, partial occlusion, low quality and small projective deformation.
UR - https://www.scopus.com/pages/publications/84893576149
U2 - 10.1109/IJCNN.2013.6706812
DO - 10.1109/IJCNN.2013.6706812
M3 - 会议稿件
AN - SCOPUS:84893576149
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -