TY - GEN
T1 - Traffic sign recognition with convolutional neural network based on max pooling positions
AU - Qian, Rongqiang
AU - Yue, Yong
AU - Coenen, Frans
AU - Zhang, Bailing
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network (CNN). In comparison with previous methods which usually use CNN as feature extractor and multi-layer perception (MLP) as classifier, we proposed max pooling positions (MPPs) as an effective discriminative feature to predict category labels. Through extensive experiments, MPPs demonstrates the ideal characteristics of small inter-class variance and large intra-class variance. Moreover, with the German Traffic Sign Recognition Benchmark (GTSRB), outstanding performance has been achieved by using MPPs.
AB - Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network (CNN). In comparison with previous methods which usually use CNN as feature extractor and multi-layer perception (MLP) as classifier, we proposed max pooling positions (MPPs) as an effective discriminative feature to predict category labels. Through extensive experiments, MPPs demonstrates the ideal characteristics of small inter-class variance and large intra-class variance. Moreover, with the German Traffic Sign Recognition Benchmark (GTSRB), outstanding performance has been achieved by using MPPs.
KW - Advanced Driver Assistance
KW - convolutional neural networks
KW - deep learning
KW - max pooling
KW - traffic sign recognition
UR - http://www.scopus.com/inward/record.url?scp=84997724808&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2016.7603237
DO - 10.1109/FSKD.2016.7603237
M3 - Conference Proceeding
AN - SCOPUS:84997724808
T3 - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
SP - 578
EP - 582
BT - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
A2 - Du, Jiayi
A2 - Liu, Chubo
A2 - Li, Kenli
A2 - Wang, Lipo
A2 - Tong, Zhao
A2 - Li, Maozhen
A2 - Xiong, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Y2 - 13 August 2016 through 15 August 2016
ER -