The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The German Traffic Sign Recognition Benchmark: A multi-class classification competition
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.
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
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Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.016 · Cited by 1,537
Self-citationMulti-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057
Metadata
- Authors
- Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C.
- Venue
- The 2011 International Joint Conference on Neural Networks
- Publisher
- IEEE
- Published
- 2011-07
- Pages
- 1453-1460
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2117876524
- Cited by
- 1,144
- References
- 2
- FWCI
- 14.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011). The German Traffic Sign Recognition Benchmark: A multi-class classification competition. The 2011 International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2011.6033395
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