Speed limit sign recognition using log-polar mapping and visual codebook

Authors:
Bing Liu;Huaping Liu;Xiong Luo;Fuchun Sun
Affiliations:
School of Computer and Communication Engineering, University of Science and Technology, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, P.R. China,State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R. China;School of Computer and Communication Engineering, University of Science and Technology, Beijing, P.R. China;Department of Computer Science and Technology, Tsinghua University, P.R. China,State Key Laboratory of Intelligent Technology and Systems, Beijing, P.R. China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 6
Cited 0

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A review of log-polar imaging for visual perception in robotics

Robotics and Autonomous Systems
Robust class similarity measure for traffic sign recognition

IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines

IEEE Transactions on Intelligent Transportation Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Traffic sign recognition is one of the hot issues on the modern driving assistance. In recent years, the method using Bag-of-Word (BOW) model for image recognition has gained its popularity upon its simplicity and efficiency. The conventional approach based on BOW requires nonlinear classifiers to get a good image recognition accuracy. Instead, a method called Locality-constrained Linear Coding(LLC) presents an effective strategy for coding, and only with a simple linear classifier could achieve a good effect. LLC uses uniform sampling for feature extraction, but allowing for features of traffic signs, the central vision information of the image is more important than the surroundings. Fortunately, log-polar mapping to preprocess image samples before coding is helpful for traffic sign recognition. In this paper, a combination method of log-polar mapping and LLC algorithm is presented to achieve a high image classification performance up to 97.3141% on speed limit sign in the GTSRB dataset.