Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Empirical Study of Learning from Imbalanced Data Using Random Forest
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Using fourier descriptors and spatial models for traffic sign recognition
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Image ranking and retrieval based on multi-attribute queries
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
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In this paper, we design the color fused multiple features to describe a traffic sign, and we further implement this description method to detect traffic signs and to classify multi-class traffic signs. At the detection stage, we utilize the GentleAdaboost classifier to separate traffic signs from the background; at the classification stage, we implement the random forest classifier to classify multi-class traffic signs. We do the extensive experiments on the popular standard traffic sign datasets: the German Traffic Sign Recognition Benchmark and the Swedish Traffic Signs Dataset. We compare eight features which include the HOG feature, the LBP feature, the color cues and their different combinations. We also compare the popular classifiers for traffic sign recognition. The experimental results demonstrate that the color fused feature achieves better classification performance than the feature without color cues, and the GentleAdaboost classifier achieves the better comprehensive performance of the binary classification, and the random forest classifier achieves the best multi-class classification accuracy.