Evaluation of traffic sign recognition methods trained on synthetically generated data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8192 LNCS, Issue , 2013, Pages 576-583

  Moiseev, Boris ^a   Konev, Artem ^a   Chigorin, Alexander ^a   Konushin, Anton ^a  

^a MOSCOW STATE UNIVERSITY   (Russian Federation)

Author keywords

nearest neighbor search; synthetic data; traffic sign recognition

Indexed keywords

COMPUTER VISION; DATA PROCESSING; LEARNING SYSTEMS;

INTRA CLASS; LABELED DATA; MACHINE LEARNING TECHNIQUES; NEAREST NEIGHBOR SEARCH; SIGN IMAGES; SYNTHETIC DATA; SYNTHETIC IMAGES; TRAFFIC SIGN RECOGNITION;

TRAFFIC SIGNS;

EID: 84890872483     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-02895-8_52     Document Type: Conference Paper

References (21)

1. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition. Neural Networks (2012)
2. Paclik, P., Novovicova, J., Duin, R.: Building Road-SignClassifiers Using a Trainable Similarity Measure. IEEE Trans. Intell. Transp. Syst. 7(3), 309-321 (2006)
3. Zaklouta, F., Stanciulescu, B., Hamdoun, O.: Traffic sign classification using K-d trees and Random Forests. In: IEEE International Joint Conference on Neural Networks, San Jose, California, pp. 2151-2155 (2011)
4. Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., Lopez-Ferreras, F.: Road-Sign Detection and Recognition Based on Support Vector Machines. IEEE Trans. Intell. Transp. Syst. 8(2), 264-278 (2007)
5. Timofte, R., Zimmermann, K., Gool, L.V.: Multi-view traffic sign detection, recognition, and 3D localization. In: Workshop on Applications of Computer Vision, Snowbird, Utah, pp. 1-8 (2009)
6. Ciresan, D., Meier, U., Masci, J., Schmindhuber, J.: A Committee of Neural Networks for Traffic Sign Classification. In: IEEE International Joint Conference on Neural Networks, San Jose, California, pp. 1918-1921 (2011)
7. Sermanet, P., Lecun, Y.: Traffic Sign Recognition with Multi-Scale Convolutional Networks. In: International Joint Conference on Neural Networks, San Jose, California, pp. 2809-2813 (2011)
8. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from a Single Depth Image. In: Proceedings IEEE Computer Vision and Pattern Recognition, Colorado, USA, pp. 1297-1304 (2011)
9. Grauman, K., Shakhnarovichand, G., Darrell, T.: Inferring 3D structure with a statistical image-based shape model. In: Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France, pp. 641-647 (2003)
10. Stark, M., Goesele, M., Schiele, B.: Back to the Future: Learning Shape Models from 3D CAD Data. In: Proceedingsof the British Machine Vision Conference, Aberystwyth, Wales, pp. 106.1-106.11 (2010)
11. Marin, J., Vazquez, D., Geronimo, D., Lopez, A.M.: Learning Appearance in Virtual Scenarios for Pedestrian Detection. In: Proceedings Computer Vision and Pattern Recognition, San Francisco, California, pp. 137-144 (2010)
12. Pishchulin, L., Thorm, T., Wojek, C., Andriluka, M., Thormahlen, T., Schiele, B.: Learning People Detection Models from Few Training Samples. In: Proceedings Computer Vision and Pattern Recognition, Colorado Springs, pp. 1-8 (2011)
13. Enzweiler, M., Gavrila, D.M.: A Mixed Generative-Discriminative Framework for Pedestrian Classification. In: Proceedings Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (2008)
14. Liebelt, J., Schmid, C., Schertler, K.: Viewpoint-Independent Object Class Detection using 3D Feature Maps. In: Proceedings Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (2008)
15. Wang, K., Babenko, B., Belongie, S.: End-to-End Scene Text Recognition. In: International Conference on Computer Vision, Barcelona, Spain (2011)
16. Ozuysal, M., Fua, P., Lepetit, V.: Fast Keypoint Recognition in Ten Lines of Code. In: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, pp. 1-8 (2007)
17. Mogelmose, A., Trivedi, M., Mouslund, T.: Learning to Detect Traffic Signs: Comparative Evaluation of Synthetic and Real-world Datasets. In: 21st International Conference on Pattern Recognition, Tsukuba, Japan (2012)
18. Overett, G.M., Tychsen-Smith, L., Petersson, L., Andersson, L., Pettersson, N.: Creating Robust High-Throughput Traffic Sign Detectors Using Centre-Surround HOG Statistics. Machine Vision and Applications Special Issue Paper, 1-14 (December 2011)
19. Medici, P., Caraffi, C., Cardarelli, E., Porta, P.P., Ghisto, G.: Real Time Road Signs Classification. In: IEEE Conference on Vehicular Electronics and Safety, Columbus, OH, USA (2008)
20. Larsson, F.: Sweden traffic signs dataset, http://www.cvl.isy.liu.se/ research/traffic-signs-dataset
21. Larsson, F., Felsberg, M.: Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition. In: Proceedings of Scandinavian Conference on Image Analysis, Ystad, Sweden, pp. 238-249 (2011)