Multiple thresholding and subspace based approach for detection and recognition of traffic sign

被引：22

作者：

Gudigar, Anjan ^{[1
]}

Chokkadi, Shreesha ^{[1
]}

Raghavendra, U. ^{[1
]}

Acharya, U. Rajendra ^{[2
,3
]}

机构：

[1] Manipal Univ, Manipal Inst Technol, Dept Instrumentat & Control Engn, Manipal 576104, Karnataka, India

[2] SIM Univ, Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore

[3] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur 50603, Malaysia

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2017年 / 76卷 / 05期

关键词：

Advanced driver assistance system; Computer vision; Multiple thresholds; Support vector machine; Traffic sign recognition;

D O I：

10.1007/s11042-016-3321-6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

引用

页码：6973 / 6991

页数：19

共 53 条

[1] Alsibai M., Hirai Y., Real-time recognition of blue traffic signs designating directions, Int J Intell Transp Syst Res, 8, 2, pp. 96-105, (2010)
[2] Barnes N., Zelinsky A., Fletcher L., Real-time speed sign detection using the radial symmetry detector, IEEE Trans Intell Transp Syst, 9, 2, pp. 322-332, (2008)
[3] Belkin M., Niyogi P., Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comput, 15, 6, pp. 1373-1396, (2003)
[4] Boumediene M., Cudel C., Basset M., Ouamri A., Triangular traffic signs detection based on rsld algorithm, Mach Vis Appl, 24, 8, pp. 1721-1732, (2013)
[5] Burges C.J.C., A tutorial on support vector machines for pattern recognition, Data Min Knowl Disc, 2, 2, pp. 121-167, (1998)
[6] Cai D., He X., Zhou K., Han J Bao H (2007) Locality sensitive discriminant analysis, Proceedings of the 20th international joint conference on artifical intelligence, pp. 708-713
[7] Chang C.C., Lin C.J., LIBSVM: a library for support vector machines, ACM Trans Intell Syst Technol, 2, pp. 27:1-27:27, (2011)
[8] Ciresan D., Meier U., Masci J., Schmidhuber J., A committee of neural networks for traffic sign classification. In: The 2011 international joint conference on neural networks, San Jose, pp 1918–1921, (2011)
[9] Duda R.O., Hart P.E., Stork D.G., Pattern classification, 2nd edn, Wiley-Interscience, (2000)
[10] Escalera S., Pujol O., Radeva P., Traffic sign recognition system with ß -correction, Mach Vis Appl, 21, 2, pp. 99-111, (2010)

← 1 2 3 4 5 6 →