ABSTRACT
Automatic traffic sign detection and recognition plays a very significant role in advance driver assistance system and intelligent transportation system. In this paper, approach for circular traffic sign detection and recognition is proposed. The entire performance of the proposed system is calculated on German Traffic Sign Detection Benchmark (GTSDB) and German Traffic Sign Recognition Benchmark (GTSRB) datasets. Traffic signs are detected on color images based on RGB color thresholding technique and further detecting circle using circular Hough Transform. In traffic sign recognition, features are extracted using Histogram of Oriented Gradient (HOG) and strong components of the image are selected by Principal Component Analysis (PCA) and classified using Ensemble of SVM as the size of the dataset is increasing day by day. Results obtained undergoes statistical test showing the better performance of the algorithm proposed.
- Berkaya, Selcan Kaplan, et al. "On circular traffic sign detection and recognition." Expert Systems with Applications 48 (2016): 67--75.Google ScholarDigital Library
- Timofte, Radu, et al. "Combining traffic sign detection with 3D tracking towards better driver assistance." Emerging topics in computer vision and its applications 1 (2011): 425--446.Google Scholar
- Ellahyani, Ayoub, Mohamed El Ansari, and Ilyas El Jaafari. "Traffic sign detection and recognition based on random forests." Applied Soft Computing(2016). Google ScholarDigital Library
- Houben, Sebastian, et al. "Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013. Google ScholarCross Ref
- Stallkamp, Johannes, et al. "The German traffic sign recognition benchmark: a multi-class classification competition." Neural Networks (IJCNN), The 2011 International Joint Conference on. IEEE, 2011. Google ScholarCross Ref
- Mogelmose, Andreas, Mohan Manubhai Trivedi, and Thomas B. Moeslund. "Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey." IEEE Transactions on Intelligent Transportation Systems 13.4 (2012): 1484--1497. Google ScholarDigital Library
- Berkaya, Selcan Kaplan, et al. "On circular traffic sign detection and recognition." Expert Systems with Applications 48 (2016): 67--75. Google ScholarDigital Library
- Yaliç, Hamdi Yalin, and Ahmet Burak Can. "Automatic recognition of traffic signs in Turkey roads." 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011.Google Scholar
- Maldonado-Bascon, Saturnino, et al. "Road-sign detection and recognition based on support vector machines." IEEE transactions on intelligent transportation systems 8.2 (2007): 264--278. Google ScholarDigital Library
- Creusen, Ivo M., et al. "Color exploitation in hog-based traffic sign detection." 2010 IEEE International Conference on Image Processing. IEEE, 2011.Google Scholar
- Shuang-dong, Zhu, Zhany Yi, and Lu Xiao-feng. "Detection for triangle traffic sign based on neural network." IEEE International Conference on Vehicular Electronics and Safety, 2005. IEEE, 2005. Google ScholarCross Ref
- De La Escalera, Arturo, et al. "Road traffic sign detection and classification." IEEE transactions on industrial electronics 44.6 (1997): 848--859.Google Scholar
- Kamada, Hiroshi, Satoshi Naoi, and Toshiyuki Gotoh. "A compact navigation system using image processing and fuzzy control." Southeastcon'90. Proceedings., IEEE. IEEE, 1990.Google Scholar
- Ritter, W. "Traffic sign recognition in color image sequences." Intelligent Vehicles' 92 Symposium., Proceedings of the. IEEE, 1992.Google Scholar
- Miura, Jun, et al. "An active vision system for on-line traffic sign recognition." IEICE TRANSACTIONS on Information and Systems 85.11 (2002):1784--1792.Google Scholar
- Shadeed, W. G., Dia I. Abu-Al-Nadi, and Mohammad Jamil Mismar. "Road traffic sign detection in color images." Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on. Vol. 2. IEEE, 2003.Google ScholarCross Ref
- De La Escalera, Arturo, et al. "Visual sign information extraction and identification by deformable models for intelligent vehicles." IEEE transactions on intelligent transportation systems 5.2 (2004): 57--68.Google Scholar
- Liu, Han, Ding Liu, and Jing Xin. "Real-time recognition of road traffic sign in motion image based on genetic algorithm." Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on. Vol. 1, IEEE, 2002.Google Scholar
- Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154 Google ScholarDigital Library
- Garcia-Garrido, Miguel Angel, Miguel Angel Sotelo, and Ernesto Martin-Gorostiza. "Fast traffic sign detection and recognition under changing lighting conditions." 2006 IEEE Intelligent Transportation Systems Conference. IEEE, 2006. Google ScholarCross Ref
- Loy, Gareth, and Nick Barnes. "Fast shape-based road sign detection for a driver assistance system." Intelligent Robots and Systems, 2004.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Conference on. Vol. 1. IEEE, 2004. Google ScholarCross Ref
- Gavrila, Dariu M. "Multi-feature hierarchical template matching using distance transforms." Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on. Vol. 1. IEEE, 1998. Google ScholarCross Ref
- Barnes, Nick, and Alex Zelinsky. "Real-time radial symmetry for speed sign detection." Intelligent Vehicles Symposium, 2004 IEEE. IEEE, 2004. Google ScholarCross Ref
- Overett, Gary, and Lars Petersson. "Large scale sign detection using HOG feature variants." Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE, 2011. Google ScholarCross Ref
- Salti, Samuele, et al. "Traffic sign detection via interest region extraction." Pattern Recognition 48.4 (2015): 1039--1049. Google ScholarDigital Library
- Yuan, Xue, et al. "Traffic sign detection via graph-based ranking and segmentation algorithms." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45.12 (2015): 1509--1521. Google ScholarCross Ref
- Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE, 2005. Google ScholarDigital Library
- Greenhalgh, Jack, and Majid Mirmehdi. "Real-time detection and recognition of road traffic signs." IEEE Transactions on Intelligent Transportation Systems 13.4 (2012): 1498--1506. Google ScholarDigital Library
- Mathias, Markus, et al. "Traffic sign recognition---How far are we from the solution?." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013. Google ScholarCross Ref
- Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." IEEE Transactions on pattern analysis and machine intelligence 24.7 (2002): 971--987. Google ScholarDigital Library
- Tang, Suisui, and Lin-Lin Huang. "Traffic sign recognition using complementary features." 2013 2nd IAPR Asian Conference on Pattern Recognition. IEEE, 2013. Google ScholarDigital Library
- Bahlmann, Claus, et al. "A system for traffic sign detection, tracking, and recognition using color, shape, and motion information." IEEE Proceedings. Intelligent Vehicles Symposium, 2005. IEEE, 2005. Google ScholarCross Ref
- Duda, R. O.; Hart, P. E.; and Stork, D. G. 2000. Pattern Classifi- cation. Hoboken, NJ: Wiley-Interscience, 2nd edition.Google ScholarDigital Library
- Claesen, Marc, et al. "EnsembleSVM: a library for ensemble learning using support vector machines." Journal of Machine Learning Research 15.1(2014):141--145.Google Scholar
Index Terms
- Ensemble of SVM for Accurate Traffic Sign Detection and Recognition
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