An Overview on Various Techniques used for Correct Interpretation of Roadway Symbols
School of Electronics Engineering (SENSE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu (T.N.), India-632014
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 28-38, 2022; DOI: 10.55708/js0103004
Keywords: Traffic Sign Recognition, Image Denoising, Filtering
Received: 11 December 2021, Revised: 25 January 2022, Accepted: 06 February 2022, Published Online: 17 March 2022
AMA Style
Deshpande AV. An overview on various techniques used for correct interpretation of roadway symbols. Journal of Engineering Research and Sciences. 2022;1(3):28-38. doi:10.55708/js0103004
Chicago/Turabian Style
Deshpande, Abhinav Vinod. “An Overview on Various Techniques Used for Correct Interpretation of Roadway Symbols.” Journal of Engineering Research and Sciences 1, no. 3 (2022): 28–38. https://doi.org/10.55708/js0103004.
IEEE Style
A. V. Deshpande, “An overview on various techniques used for correct interpretation of roadway symbols,” Journal of Engineering Research and Sciences, vol. 1, no. 3, pp. 28–38, 2022.
In this study paper, a comparative analysis of various image enhancements in local domain techniques was made based on three-dimensional image quality statistics namely Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Quality Model (SSIM) to determine image quality. especially among them so that the process of image enhancement in the local domain is carried out. The conditions for choosing the best method are to have a small number of Mean Squared Error (MSE) and a high number of Peak Signal to Noise Ratio (PSNR) and Structural Properties to measure Image Quality (SSIM).
- Huaping Liu, Yulong Liu, Fuchun Sun, “Traffic sign recognition using group sparse coding”, Elsevier- Information Sciences, Volume 266, January 2021, pp. 75-89.
- Zhan-Li Sun, Han Wang, Wai-Shing Lau, Gerald Seet, Danwei Wang, “Application of BW-ELM model on traffic signrecognition”, Elsevier- Neurocomputing, Volume 128, October 2021, pp. 153-159.
- Fatin Zaklouta, Bogdan Stanciulescu, “Real-time traffic sign recognition in three stages”, Elsevier- Robotics and Autonomous Systems, Volume 62, August 2021, pp. 16-24.
- Shuihua Wang, Hangrong Pan, Chenyang Zhang, Yingli Tian, “RGB-D image-based detection of stairs, pedestrian crosswalks and traffic Signs”, Elsevier- Journal of Visual Communication and Image Retrieval, Volume 25, November 2020, pp. 263-272.
- Jonathan J. Kay, Peter T. Savolainen, Timothy J. Gates, Tapan K. Datta, “Driver behaviour during bicycle passing manoeuvres in response to aShare the Road sign treatment”, Elsevier-Accident Analysis and Prevention, Volume 70, April 2021, pp. 92-99.
- Jesmin Khan, Sharif Bhuiyan, Reza Adhami, “Hierarchical clustering of EMD based interest points for road sign detection”, Elsevier- Optics & Laser Technology, Volume 57, October 2021, pp. 271-283.
- Zong-Yao Chen, Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai, “Evolutionary feature and instance selection for traffic sign recognition”, Elsevier- Computers in Industry, Volume 74, September 2020, pp. 201-211.
- Samuele Salti, Alioscia Petrelli, Federico Tombari, Nicola Fioraio, Luigi Di Stefano, “Traffic sign detection via interest region extraction”, Elsevier- Pattern Recognition, Volume 48, June 2020, pp. 1039-1049.
- M. Lillo-Castellano, I. Mora-Jiménez, C. Figuera-Pozuelo, J.L. Rojo-Álvarez, “Traffic sign segmentation and classification using statistical learning methods”, Elsevier- Neurocomputing, Volume 153, November 2021, pp. 286-299.
- Haojie Li, Fuming Sun, Lijuan Liu, Ling Wang, “A novel traffic sign detection method via colour segmentation and robust shape matching”, Elsevier- Neurocomputing, Volume 169, May 2021, pp. 77-88.
- Zhenyu An, Zhenwei Shi, Ying Wu, Changshui Zhang, “A novel unsupervised approach to discovering regions of interest in traffic images”, Elsevier- Pattern Recognition, Volume 48, February 2020, pp. 2581-2591.
- Yingying Zhu, Chengquan Zhang, Duoyou Zhou, Xinggang Wang, Xiang Bai, Wenyu Liu, “Traffic sign detection and recognition using fully convolutional Network guided proposals”, Elsevier- Neurocomputing, Volume 214, July 2021, pp. 758- 766.
- Ayoub Ellahyani, Mohamed El Ansari, Ilyas El Jaafari, “Traffic sign detection and recognition based on random forests”, Elsevier- Applied Soft Computing, Volume 46, February 2020, pp. 805-815.
- Selcan Kaplan Berkaya, Huseyin Gunduz, Ozgur Ozsen, Cuneyt Akinlar, Serkan Gunal, “On circular traffic sign detection and recognition”, Elsevier- Expert Systems with Applications, Volume 48, 2020, pp. 67-75.
- Yongtao Yu, Jonathan Li, Chenglu Wen, Haiyan Guan, HuanLuo, Cheng Wang, “Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data”, Elsevier- ISPRS Journal of Photogrammetry and Remote Sensing, Volume 113, January 2021, pp. 106-123.
- Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig, “A practical approach for detection and classification of traffic signs using Convolutional Neural Networks”, Elsevier- Robotics and Autonomous Systems, Volume 84, July 2020, pp. 97-112.
- Mario Soilán, Belen Riveiro, Joaquin Martinez-Sanchez, Pedro Arias, “Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory”, Elsevier- ISPRS Journal of Photogrammetry and Remote Sensing, Volume 114, February 2020, pp. 92-101.
- Ouerhani, A. Alfalou, M. Desthieux, C. Brosseau, “Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique”, Elsevier- Optics and Lasers in Engineering, Volume 89, May 2021, pp. 184-194.
- Jack Greenhalgh and Majid Mirmehdi, “Recognizing Text-Based Traffic Signs”, IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 3, June 2020, pp. 1360-1369.
- Nadra Ben Romdhane, Hazar Mliki, Mohamed Hammami, “An Improved Traffic Signs Recognition and Tracking Method for Driver Assistance System”, in the Proceedings of IEEE International Conference on Information Security (ICIS’2020), June 26- 29, 2020, Okayama, Japan.
- Ruben Laguna, Ruben Barrientos, L. Felipe Blazquez, Luis J. Miguel, “Traffic sign recognition application based on image processing techniques”, in the Proceedings of the Elsevier- Proceedings of the 19th World Congress, The International Federation of Automatic Control, Cape Town, South Africa. August 24-29, 2021, pp. 104-109.
- Baro, S. Escalera, J. Vitria, O. Pujol, P. Radeva, “Traffic sign recognition using evolutionary adaboost detection and forest- ecoc classification”, IEEE Transactions on Intelligent Transport Systems, Volume 10, Issue 1, 2020, pp. 113-126.
- M. Bascon, J.A. Rodriguez, S.L. Arroyo, A.F. Caballero, F. Lopez-Ferreras, “An optimization on pictogram identification for the road-sign recognition task using SVMs”, Computer Vision Image Understanding, Volume 114, Issue 3, 2021, pp. 373-383.
- C. Chang, Y.P. Hsieh, “A fast VQ codebook search with initialization and search order”, Information Sciences, Volume 183, Issue 1, 2021, pp. 132-139.
- Ciresan, U. Meier, J. Mascim, J. Schmidhuber, “A committee of neural networks for traffic signs classification”, in Proceedings of International Joint Conference on Neural Networks (IJCNN), July 2020, pp. 1918-1921.
- Escalera, O. Pujol, P. Radeva, “Traffic sign recognition system with b-correction”, Elsevier- Machine Vision Applications, Volume 21, Issue 2, 2020, pp. 99-111.
- Gomez-Moreno, S. Maldonado-Bascon, P. Gil-Jimenez, S. Lafuente-Arroyo, “Goal evaluation of segmentation algorithms for traffic sign recognition”, IEEE Transactions on Intelligent Transportation Systems, Volume 11, Issue 4, 2021, pp. 917-930.
- Gu, T. Yendo, M.P. Tehrani, T. Fujii, M. Tanimoto, “Traffic sign detection in dual-focal active camera system”, in the Proceedings of IEEE Intelligent Vehicles Symposium (IV), 2020, pp. 1054-1059.
- Gunal, R. Edizkan, “Subspace based feature selection for pattern recognition”, Elsevier-Information Sciences, Volume 178, 2021, pp. 3716-3726.
- Huang, K. Huang, Y. Yu, T. Tan, “Salient coding for image classification”, in the Proceedings of Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1753-1760.
- K. Chung, ”A Phase Tracking System for Three Phase Utility Interface Inverters”, IEEE Trans. On Power Electronics, Vol. 15, pp. 431-438, May 2000.
- Hassanabad, A. H., & Nazeipur, D. Design and Simulation of a Control System for Investors in Wind Turbines. Vol. 6, No. 3, 2021, pp. 31-36.
- Jun-min, L. K. P. Z., & Yang, X. U. A. N. (2003). Harmonics detection for three-phase circuits based on resampling theory and mean filtering. Proceedings of the CSEE.