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Traffic Sign Recognition - How far are we from the solution?

Markus Mathias, Radu Timofte, Rodrigo Benenson and Luc Van Gool
Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2013)
Dallas, USA, August 2013

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.


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@InProceedings{eth_biwi_01052,
  author = {Markus Mathias and Radu Timofte and Rodrigo Benenson and Luc Van Gool},
  title = {Traffic Sign Recognition - How far are we from the solution?},
  booktitle = {Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2013)},
  year = {2013},
  month = {August},
  keywords = {}
}