Indonesian traffic sign detection based on Haar-PHOG features and SVM classification
References
- Adnan, A. W., Yussof, S. and Mahmood, S. 2015. Soft biometrics: gender recognition from unconstrained face images using local feature descriptor. Journal of Information and Communication Technology 14: 111–122, doi: 10.1145/1282280.1282340.
- Arora, S., Brar, Y. S. and Kumar, S. 2014. Haar wavelet transform for solution of image retrieval. International Journal of Advanced Computer and Mathematical Science 5: 27–31.
- Banerji, S., Sinha, A. and Liu, C. 2013a. New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117: 173–185, doi: 10.1016/j.neucom.2013.02.014.
- Banerji, S., Sinha, A. and Liu, C. 2013b. HaarHOG: improving the HOG descriptor for image classification. 2013 IEEE International Conference on Systems, Man, and Cybernetics HaarHOG, 4282–4287, doi: 10.1109/SMC.2013.729.
- Berkaya, S. K., Gunduz, H., Ozsen, O., Akinlar, C. and Gunal, S. 2016. On circular traffic sign detection and recognition. Expert Systems with Applications 48: 67–75, doi: 10.1016/j.eswa.2015.11.018.
- Biswas, R. and Tora, M. R. 2014. LVQ and HOG based speed limit traffic signs detection and categorization. 3rd International Conference on Informatics, Electronics & Vision, 2014, 1–6, doi: 1109/iciev.2014.6850741.
- Bosch, A. and Zisserman, A. 2007. “Representing shape with a spatial pyramid kernel”, Proceedings of the ACM International Conference on Image and Video Retrieval, Amsterdam, July 9–11, doi: 10.1145/1282280.1282340.
- Chen, Y., Xie, Y. and Wang, Y. 2013. Detection and recognition of traffic signs based on HSV vision model and shape features. Journal of Computing 8(5): 1366–1370, doi: 10.4304/jcp.8.5.1366-1370.
- Dai, W., Jiang, J., Ding, G. and Liu, Z. 2019. Development and application of fire video image detection technology in China’s road tunnels. Civil Engineering Journal 5(1): 1–17, doi: 10.28991/cej-2019-03091221.
- Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. Proceeding 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, Vol. I, 886–893, doi: 10.1109/CVPR.2005.177.
- Daubechies, I. 1992. Ten lectures on wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, Lowell, MA, June 1990, doi: 10.1137/1.9781611970104.
- Ellahyani, A., El Ansari, M. and Jaafari, I. El 2016. Traffic sign detection and recognition based on random forests. Applied Soft Computing 46: 805–815, doi: 10.1016/j.asoc.2015.12.041.
- Escalera, S., Baro, X., Pujol, O., Vitria, J. and Radeva, P. 2011. Traffic-sign Recognition Systems Springer, London, doi: 10.1007/978-1-4471-2245-6_5.
- Espejel-García, D., Ortíz-Anchondo, L. R., Alvarez-Herrera, C., Hernandez-López, A., Espejel-García, V. V. and Villalobos-Aragón, A. 2017. An alternative vehicle counting tool using the Kalman filter within MATLAB. Civil Engineering Journal 3(11): 1029–1035, doi: 10.28991/cej-030935.
- Fleyeh, H. 2013. “Traffic sign detection based on AdaBoost color segmentation and SVM classification”, Eurocon, Zagreb, July 1-4, pp. 2005–2010, doi: 10.1109/eurocon.2013.6625255.
- Fleyeh, H. 2015. Traffic sign recognition without color information. Colour and Visual Computing Symposium (CVCS) IEEE, Gjøvik, August 25-26, doi: 10.1109/cvcs.2015.7274886.
- Han, Y., Virupakshappa, K. and Oruklu, E. 2015. Robust traffic sign recognition with feature extraction and k-NN classification methods. IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL, May 21-23, pp. 484–488, doi: 10.1109/EIT.2015.7293386.
- He, X. and Dai, B. 2016. A new traffic signs classification approach based on local and global features extraction. 2016 6th International Conference on Information Communication and Management A, 121–125, doi: 10.1109/infocoman.2016.7784227.
- Kalistatov, K. D. 2019. Wireless video monitoring of the megacities transport infrastructure. Civil Engineering Journal 5(5): 1033–1040, doi: 10.28991/cej-2019-03091309.
- Kassani, P. H., Hyun, J. and Kim, E. 2016. Application of soft histogram of oriented gradient on traffic sign detection. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAl), 388–392, doi: 10.1109/URAI.2016.7734067.
- Li, H., Sun, F., Liu, L. and Wang, L. 2015. A novel traffic sign detection method via color segmentation and robust shape matching. Neurocomputing 169: 77–88, doi: 10.1016/j.neucom.2014.12.111.
- Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H. and Lopez-Ferreras, F. 2007. Road-sign detection and recognition based on support vector machines. IEEE Transactions on Intelligent Transportation Systems 8(2): 264–278, doi: 10.1109/TITS.2007.895311.
- Mogelmose, A., Trivedi, M. M. and Moeslund, T. B. 2012. Vision-based traffic sign detection and analysis for intelligent driver assistance systems. Perspectives and Survey 13(4): 1484–1497, doi: 10.1109/tits.2012.2209421.
- Ojala, T., Pietikäinen, M. and Mäenpää, T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7): 971–987, doi: 10.1109/TPAMI.2002.1017623.
- Razian, S. A. and Mahvash Mohammadi, H. 2017. Optimizing raytracing algorithm using CUDA. Italian Journal of Science & Engineering 1(3): 167–178, doi: 10.28991/ijse-01119.
- Ruta, A., Li, Y. and Liu, X. 2010. Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recognition 43(1): 416–430, doi: 10.1016/j.patcog.2009.05.018.
- Shengchao, F., Le, X. I. N. and Yangzhou, C. 2014. Traffic sign detection based on co-training method. Proceedings of the 33rd Chinese Control Conference, July 28-30, Nanjing, China Traffic, 4893–4898, doi: 10.1109/chicc.2014.6895769.
- Soetedjo, A. and Somawirata, I. K. 2017. Circular traffic sign classification using hogbased ring partitioned matching. International Journal of Smart Sensing and Intelligent Systems 10(3): 735–753, doi: 10.21307/ijssis-2017-232.
- Wahyono, W. and Jo, K. 2014. “A comparative study of classification methods for traffic signs recognition”, 2014 IEEE International Conference on Industrial Technology (ICIT), Vol. 1, pp. 614–619, doi: 10.1109/icit.2014.6895001.
- Wang, Q. 2014. Traffic sign segmentation in natural scenes based on color and shape features. 2014 IEEE Workshop on Advanced Research and Technology in Industry Application Traffic, 374–377, doi: 10.1109/wartia.2014.6976273.
- World Health Organization 2018. Global status report on road safety 2018. World Health Organization, Geneva.
- Zaklouta, F. and Stanciulescu, B. 2012. Real-time traffic-sign recognition using tree classifiers. IEEE Transactions on Intelligent Transportation Systems 13(4): 1507–1514, doi: 10.1109/TITS.2012.2225618.
- Zaklouta, F., Stanciulescu, B. and Mask, A. F. 2011. Real-time traffic sign recognition using spatially weighted HOG trees. The 15th International Conference on Advanced Robotics, Tallinn, June 20-23, doi: 10.1109/icar.2011.6088571.
DOI: https://doi.org/10.21307/ijssis-2020-026 | Journal eISSN: 1178-5608
Language: English
Page range: 1 - 15
Submitted on: Jun 3, 2020
Published on: Oct 5, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2020 Aris Sugiharto, Agus Harjoko, Suharto Suharto, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.