Abstract
The "German Traffic Sign Recognition Benchmark" is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.
| Original language | English |
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| Title of host publication | Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN) |
| Number of pages | 8 |
| Publisher | IEEE |
| Publication date | 2011 |
| Pages | 1453-1460 |
| ISBN (Print) | 978-1-4244-9635-8 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 2011 International Joint Conference on Neural Networks - San Jose, California , United States Duration: 31 Jul 2011 → 5 Aug 2011 |
Conference
| Conference | 2011 International Joint Conference on Neural Networks |
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| Country/Territory | United States |
| City | San Jose, California |
| Period | 31/07/2011 → 05/08/2011 |