Implementación de un algoritmo para la detección de señales de tránsito del Ecuador: Pare, Ceda el paso y Velocidad
Main Article Content
Abstract
This paper presents a prototype for a traffic sign detection system (TSDS) on-board a moving vehicle. Therefore, a new approach to the development of an TSDS is presented, using the following innovations: i) an efficient method of color segmentation for the generation of regions of interest (ROIs) based on k-NN with Km-means , ii) a new version of the HOG descriptor for feature extraction and iii) SVM training for stage multi-classification. The proposed approach has been specialized and tested on a subset of regulatory Ecuadorian signs (Stop, Give-way and Speed). Many experiments have been carried out in real driving conditions, under different lighting changes such as normal, sunny and cloudy. This system has showed a global performance of 98.7% for segmentation, 99.49% for classification and an accuracy of 96% for detection.
Keywords
accidents accidentes, Ecuador, HOG, k-NN, Km-means, SVM, pare, ceda el paso, velocidad
References
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[25] M. M. Lau, K. H. Lim, and A. A. Gopalai, “Malaysia traffic sign recognition with convolutional neural network,” in 2015 IEEE International Conference on Digital Signal Processing (DSP), July 2015, pp. 1006–1010. doi: https://doi.org/10.1109/ICDSP.2015.7\protect \kern +.1667em\relax 252\protect \kern +.1667em\relax029.
[26] G. P. Martinsanz and J. M. de la Cruz García, Visión por computador: imágenes digitales y aplicaciones, R.-M. S. E. y Publicaciones, Ed., 2008. [Online]. Available: https://goo.gl/YDjJG6
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[28] Z. Zuo, K. Yu, Q. Zhou, X. Wang, and T. Li, “Traffic signs detection based on faster r-cnn,” in 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), June 2017, pp. 286–288. doi: https://doi.org/10.1109/ICDCSW.2017.34.
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[35] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, ser. IJCAI’95. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1995, pp. 1137–1143. [Online]. Available: https://goo.gl/j5rXZX
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[2] OMS. (2018) Lesiones causadas por el tránsito. Organizaciíon Mundial de la Salud. [Online]. Available: https://goo.gl/q1Mbuf
[3] K. Schwab, “The global competitiveness report 2015–2016,” World Economic Forum, Tech. Rep., 2015. [Online]. Available: https://goo.gl/FvJVkb
[4] ANT. (2017) Siniestros septiembre 2017. Agencia Nacional de Tránsito, Ecuador. [Online]. Available: https://goo.gl/MZ1d75
[5] ——. (2015) Siniestros octubre 2015. Agencia Nacional de Tránsito, Ecuador. [Online]. Available: https://goo.gl/Pc4JfM
[6] B. T. Nguyen, S. J. Ryong, and K. J. Kyu, “Fast traffic sign detection under challenging conditions,” in 2014 International Conference on Audio, Language and Image Processing, July 2014, pp. 749–752. doi: https://doi.org/10.1109/ICALIP.2014.7\protect \kern +.1667em\relax 009\protect\kern +.1667em\relax 895.
[7] H. Gomez-Moreno, S. Maldonado-Bascon, P. Gil-Jimenez, and S. Lafuente-Arroyo, “Goal evaluation of segmentation algorithms for traffic sign recognition,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 917–930, 2010. doi: https://doi.org/10.1109/TITS.2010.2054084.
[8] A. Shaout, D. Colella, and S. Awad, “Advanced driver assistance systems - past, present and future,” in Computer Engineering Conference (ICENCO), 2011 Seventh International, Dec 2011, pp. 72–82. doi: https://doi.org/10.1109/ICENCO.2011.6\protect \kern +.1667em\relax 153\protect\kern +.1667em\relax 935.
[9] A. Møgelmose, D. Liu, and M. M. Trivedi, “Detection of u.s. traffic signs,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 6, pp. 3116–3125, Dec 2015. doi: https://doi.org/10.1109/TITS.2015.2433019.
[10] S. E. Perez-Perez, S. E. Gonzalez-Reyna, S. E. Ledesma-Orozco, and J. G. Avina-Cervantes, “Principal component analysis for speed limit traffic sign recognition,” in 2013 IEEE International Autumn Meeting on Power Electronics and Computing (ROPEC), Nov 2013, pp. 1–5. doi: https://doi.org/10.1109/ROPEC.2013.6\protect \kern+.1667em\relax 702\protect \kern+.1667em\relax 716.
[11] S. Salti, A. Petrelli, F. Tombari, N. Fioraio, and L. D. Stefano, “Traffic sign detection via interest region extraction,” Pattern Recognition, vol. 48, no. 4, pp. 1039–1049, 2015. doi: https://doi.org/10.1016/j.patcog.2014.05.017.
[12] H. Li, F. Sun, L. Liu, and L. Wang, “A novel traffic sign detection method via color segmentation and robust shape matching,” Neurocomputing, vol. 169, pp. 77–88, 2015. doi: https://doi.org/10.1016/j.neucom.2014.12.111.
[13] T. hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, S. S. B. Media, Ed., 2009. [Online]. Available: https://goo.gl/5kPw8C
[14] J. Lillo-Castellano, I. Mora-Jiménez, C. Figuera-Pozuelo, and J. Rojo-Álvarez, “Traffic sign segmentation and classification using statistical learning methods,” Neurocomputing, vol. 153, pp. 286–299, 2015. doi: https://doi.org/10.1016/j.neucom.2014.11.026.
[15] T. Chen and S. Lu, “Accurate and efficient traffic sign detection using discriminative adaboost and support vector regression,” IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4006–4015, June 2016. doi: https://doi.org/10.1109/TVT.2015.2500275.
[16] Y. Han, K. Virupakshappa, and E. Oruklu, “Robust traffic sign recognition with feature extraction and k-nn classification methods,” in 2015 IEEE International Conference on Electro/Information Technology (EIT), May 2015, pp. 484–488. doi: https://doi.org/10.1109/EIT.2015.7\protect \kern +.1667em\relax 293\protect \kern
+.1667em\relax 386.
[17] G. Villalón-Sepúlveda, M. Torres-Torriti, and M. Flores-Calero, “Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado,” Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 14, no. 2, pp. 152–162, 2017. doi: https://doi.org/10.1016/j.riai.2016.09.010.
[18] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, Sep 1995. doi: https://doi.org/10.1007/BF00994018.
[19] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed., J. W. Sons, Ed., 2012. [Online]. Available: https://goo.gl/pvWsE9
[20] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000. doi: https://doi.org/10.1017/CBO9780511801389.
[21] Z. Huang, Y. Yu, and J. Gu, “A novel method for traffic sign recognition based on extreme learning machine,” in Proceeding of the 11th World Congress on Intelligent Control and Automation, June 2014, pp. 1451–1456. doi: https://doi.org/10.1109/WCICA.2014.7\protect \kern +.1667em\
relax 052\protect \kern +.1667em\relax 932.
[22] J. H. Shi and H. Y. Lin, “A vision system for traffic sign detection and recognition,” in 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), June 2017, pp. 1596–1601. doi: https://doi.org/10.1109/ISIE.2017.8\protect \kern +.1667em\relax 001\protect \kern+.1667em\relax 485.
[23] N. Dalal, “Finding People in Images and Videos,” Theses, Institut National Polytechnique de Grenoble - INPG, 2006. [Online]. Available: https://goo.gl/ZZCd7F
[24] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-up robust features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008. doi: https://doi.org/10.1016/j.cviu.2007.09.014.
[25] M. M. Lau, K. H. Lim, and A. A. Gopalai, “Malaysia traffic sign recognition with convolutional neural network,” in 2015 IEEE International Conference on Digital Signal Processing (DSP), July 2015, pp. 1006–1010. doi: https://doi.org/10.1109/ICDSP.2015.7\protect \kern +.1667em\relax 252\protect \kern +.1667em\relax029.
[26] G. P. Martinsanz and J. M. de la Cruz García, Visión por computador: imágenes digitales y aplicaciones, R.-M. S. E. y Publicaciones, Ed., 2008. [Online]. Available: https://goo.gl/YDjJG6
[27] Y. Zhu, C. Zhang, D. Zhou, X. Wang, X. Bai, and W. Liu, “Traffic sign detection and recognition using fully convolutional network guided proposals,” Neurocomputing, vol. 214, pp. 758–766, 2016. doi: https://doi.org/10.1016/j.neucom.2016.07.009.
[28] Z. Zuo, K. Yu, Q. Zhou, X. Wang, and T. Li, “Traffic signs detection based on faster r-cnn,” in 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), June 2017, pp. 286–288. doi: https://doi.org/10.1109/ICDCSW.2017.34.
[29] T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics, vol. 3, no. 1, pp. 1–27, 1974. doi: https://doi.org/10.1080/03610927408827101.
[30] D. L. Davies and D. W. Bouldin, “A cluster separation measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, pp. 224–227, April 1979. doi: https://doi.org/10.1109/TPAMI.1979.4766909.
[31] T. Robert, W. Guenther, and H. Trevor, “Estimating the number of clusters in a data set via the gap statistic,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 63, no. 2, pp. 411–423. doi: https://doi.org/10.1111/1467--9868.00\protect \kern +.1667em\relax 293,2001.
[32] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987. doi: https://doi.org/10.1016/0377-0427(87)90125-7.
[33] T. Fawcett, “Roc graphs: Notes and practical considerations for researchers,” Tech. Rep., 2004. [Online]. Available: https://goo.gl/Sr6tFj
[34] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, June 2005, pp. 886–893. doi: https://doi.org/10.1109/CVPR.2005.177.
[35] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, ser. IJCAI’95. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1995, pp. 1137–1143. [Online]. Available: https://goo.gl/j5rXZX
[36] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, “The det curve in assessment of detection task performance,” 1997, pp. 1895–1898. [Online]. Available: https://goo.gl/nf6MZu
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Jun 30, 2018
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Scientific Paper