Traffic sign recognition — How far are we from the solution?
Top Cited Papers
- 1 August 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21614393,p. 1-8
- https://doi.org/10.1109/ijcnn.2013.6707049
Abstract
Traffic sign recognition has been a recurring application domain for visual objects detection. The public datasets have only recently reached large enough size and variety to enable proper empirical studies. We revisit the topic by showing how modern methods perform on two large detection and classification datasets (thousand of images, tens of categories) captured in Belgium and Germany. We show that, without any application specific modification, existing methods for pedestrian detection, and for digit and face classification; can reach performances in the range of 95% ~ 99% of the perfect solution. We show detailed experiments and discuss the trade-off of different options. Our top performing methods use modern variants of HOG features for detection, and sparse representations for classification.This publication has 20 references indexed in Scilit:
- Multi-column deep neural network for traffic sign classificationNeural Networks, 2012
- Pedestrian detection at 100 frames per secondPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Traffic sign recognition with multi-scale Convolutional NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Integral Channel FeaturesPublished by British Machine Vision Association and Society for Pattern Recognition ,2009
- Classification using intersection kernel support vector machines is efficientPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- SRDA: An Efficient Algorithm for Large-Scale Discriminant AnalysisIEEE Transactions on Knowledge and Data Engineering, 2007
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- PCA versus LDAIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- Support-vector networksMachine Learning, 1995
- THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTSAnnals of Eugenics, 1938