Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition

Neural Networks - Tập 32 - Trang 323-332 - 2012
J. Stallkamp1, M. Schlipsing1, J. Salmen1, C. Igel2
1Institut für Neuroinformatik, Ruhr-Universität Bochum, Universitätsstraße 150, 44780 Bochum, Germany
2Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark

Tài liệu tham khảo

Bahlmann, 2005, A system for traffic sign detection, tracking, and recognition using color, shape, and motion information, 255 Bayer, 1975 Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 Broggi, 2007, Real time road signs recognition, 981 Ciresan, 2011, A committee of neural networks for traffic sign classification, 1918 Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 886–893). Gao, 2006, Recognition of traffic signs based on their colour and shape features extracted using human vision models, Journal of Visual Communication and Image Representation, 17, 675, 10.1016/j.jvcir.2005.10.003 Gunturk, 2005, Demosaicking: color filter array interpolation, IEEE Signal Processing Magazine, 22, 44, 10.1109/MSP.2005.1407714 Hastie, 2001 Igel, 2008, Shark, Journal of Machine Learning Research, 9, 993 Keller, 2008, Real-time recognition of US speed signs, 518 Lyu, 2008, Nonlinear image representation using divisive normalization, 1 Maldonado Bascón, 2010, An optimization on pictogram identification for the road-sign recognition task using SVMs, Computer Vision and Image Understanding, 114, 373, 10.1016/j.cviu.2009.12.002 Moutarde, 2007, Robust on-vehicle real-time visual detection of American and European speed limit signs with a modular traffic signs recognition system, 1122 Muhammad, 2009, Analysis of speed sign classification algorithms using shape based segmentation of binary images, Vol. 5702, 1220 Pinto, 2008, Why is real-world visual object recognition hard?, PLoS Computational Biology, 4, e27, 10.1371/journal.pcbi.0040027 Ramanath, 2002, Demosaicking methods for Bayer color arrays, Journal of Electronic Imaging, 11, 306, 10.1117/1.1484495 Ruta, 2010, Real-time traffic sign recognition from video by class-specific discriminative features, Pattern Recognition, 43, 416, 10.1016/j.patcog.2009.05.018 Salmen, 2010, Efficient update of the covariance matrix inverse in iterated linear discriminant analysis, Pattern Recognition Letters, 31, 1903, 10.1016/j.patrec.2010.03.001 Scharstein, 2002, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47, 7, 10.1023/A:1014573219977 Sermanet, 2011, Traffic sign recognition with multi-scale convolutional networks, 2809 Stallkamp, 2011, The German traffic sign recognition benchmark: a multi-class classification competition, 1453 Vapnik, 2009, A new learning paradigm: learning using privileged information, Neural Networks, 22, 544, 10.1016/j.neunet.2009.06.042 Viola, 2001, Robust real-time object detection, International Journal of Computer Vision, 57, 137, 10.1023/B:VISI.0000013087.49260.fb Zaklouta, 2011, Traffic sign classification using k-d trees and random forests, 2151