Machine Learning
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
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
The Journal of Machine Learning Research
Analysis of Speed Sign Classification Algorithms Using Shape Based Segmentation of Binary Images
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
Pattern Recognition Letters
Color fused multiple features for traffic sign recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Application of BW-ELM model on traffic sign recognition
Neurocomputing
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
Boosting-SVM: effective learning with reduced data dimension
Applied Intelligence
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Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data-and the CNNs outperformed the human test persons.