Russian traffic sign images dataset
V.I. Shakhuro, A.S. Konushin
NRU Higher School of Economics, Moscow, Russia,
Lomonosov Moscow State University, Moscow, Russia
Full text of article: Russian language.
A new public dataset of traffic sign images is presented. The dataset is intended for training and testing the algorithms of traffic sign recognition. We describe the dataset structure and guidelines for working with the dataset, comparing it with the previously published traffic sign datasets. The evaluation of modern detection and classification algorithms conducted using the proposed dataset has shown that existing methods of recognition of a wide class of traffic signs do not achieve the accuracy and completeness required for a number of applications.
traffic sign dataset, traffic sign classification and detection, cascade of weak classifiers, convolutional neural network.
Shakhuro VI, Konushin AS. Russian traffic sign images dataset. Computer Optics 2016; 40(2): 294-300. DOI: 10.18287/2412-6179-2016-40-2-294-300.
- Stallkamp J, Schlipsing M, Salmen J, Igel C. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural networks 2012; 32: 323-332.
- Houben S, Stallkamp J, Salmen J, Schlipsing M, Igel C. Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark. The International Joint Conference Neural Networks; 2013: 1-8.
- Larsson F, Felsberg M. Using Fourier descriptors and spatial models for traffic sign recognition. Image Analysis 2011; 238-249.
- Timofte R, Zimmermann K, Van Gool L. Multi-view traffic sign detection, recognition, and 3d localisation. Machine Vision and Applications 2014; 25(3): 633-647.
- Mogelmose A, Trivedi MM, Moeslund TB. Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey. IEEE Transactions on Intelligent Transportation Systems 2012; 13(4): 1484-1497.
- Yakimov PYu. Preprocessing of digital images in systems of location and recognition of road signs. Computer Optics 2013; 37(3): 401-405.
- Ruta A, Li Y, Porikli F, Watanabe S, Kage H, Sumi K. A New Approach for In-Vehicle Camera Traffic Sign Detection and Recognition. Machine Vision and Applications 2009; 509-513.
- Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2001: 511-518.
- Dollár P, Appel R, Kienzle W. Crosstalk cascades for frame-rate pedestrian detection. Computer Vision (ECCV); 2012: 645-659.
- Dollár P, Appel R, Belongie S, Perona P. Fast feature pyramids for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2014; 36(8):1532-1545.
- Benenson R, Mathias M, Timofte R, Van Gool L. Pedestrian detection at 100 frames per second. IEEE Conference on Computer Vision and Pattern Recognition; 2012: 2903-2910.
- Overett G, Tychsen-Smith L, Petersson L, Pettersson N, Andersson L. Creating robust high-throughput traffic sign detectors using centre-surround HOG statistics. Machine Vision and Applications 2014; 25(3): 713-726.
- Mathias M, Timofte R, Benenson R, Van Gool L. Traffic sign recognition – How far are we from the solution? International Joint Conference on Neural Networks; 2013: 1-8.
- Dalal N, Triggs B. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2005: 886-893.
- Lisitsyn SO, Bayda OA. Road sign recognition using support vector machines and histogram of oriented gradients. Computer Optics 2012; 36(2): 289-295.
- Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems; 2012: 1097-1105.
- Ciresan D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural Networks 2012; 32: 333-338.
- Moiseev B, Konev A, Chigorin A, Konushin A. Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data. Advanced Concepts for Intelligent Vision Systems; 2013: 576-583.
- Chigorin A, Konushin A. A system for large-scale automatic traffic sign recognition and mapping. CMRT13 – City Models, Roads and Traffic 2013 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) 2013; 3: 13-17.
- Li H, Lin Z, Shen X, Brandt J, Hua G. A convolutional neural network cascade for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015: 5325-5334.
- Dollár P. Piotr’s image and video Matlab Toolbox (PMT). Source: áhttp://vision.ucsd.edu/~pdollar/toolbox/doc/index.htmlñ.
- Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional architecture for fast feature embedding. Proceedings of the ACM International Conference on Multimedia; 2014: 675-678.
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