The German traffic sign recognition benchmark: a multi-class classification competition

TY - GEN

T1 - The German traffic sign recognition benchmark

T2 - 2011 International Joint Conference on Neural Networks

AU - Stallkamp, Johannes

AU - Schlipsing, Marc

AU - Salmen, Jan

AU - Igel, Christian

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

U2 - 10.1109/IJCNN.2011.6033395

DO - 10.1109/IJCNN.2011.6033395

M3 - Article in proceedings

SN - 978-1-4244-9635-8

SP - 1453

EP - 1460

BT - Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

Y2 - 31 July 2011 through 5 August 2011

ER -