{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:45:46Z","timestamp":1763747146972,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2012,9,5]],"date-time":"2012-09-05T00:00:00Z","timestamp":1346803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network.<\/jats:p>","DOI":"10.3390\/s120912153","type":"journal-article","created":{"date-parts":[[2012,9,5]],"date-time":"2012-09-05T11:40:07Z","timestamp":1346845207000},"page":"12153-12168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Recognition Stage for a Speed Supervisor Based on Road Sign Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Juan-Pablo","family":"Carrasco","sequence":"first","affiliation":[{"name":"Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2618-857X","authenticated-orcid":false,"given":"Arturo de la Escalera","family":"De la Escalera","sequence":"additional","affiliation":[{"name":"Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain"}]},{"given":"Jos\u00e9 Mar\u00eda","family":"Armingol","sequence":"additional","affiliation":[{"name":"Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2012,9,5]]},"reference":[{"key":"ref_1","unstructured":"Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E., and Mathers, C. (2004). World Report on Road Traffic Injury Prevention, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kopits, E., and Cropper, M. (2003). Traffic Fatalities and Economic Growth, The World Bank.","DOI":"10.1596\/1813-9450-3035"},{"key":"ref_3","unstructured":"Murray, C.J.L., and Lopez, A.D. (1996). The Global Burden of Disease, Harvard University Press."},{"key":"ref_4","unstructured":"Carrasco, J.P., De la Escalera, A., and Armingol, J. (September, January 22\u2013). Driving Supervision through Traffic Sign Analysis. Columbus, OH, USA."},{"key":"ref_5","unstructured":"Lauziere, Y.B., Gingras, D., and Ferrie, F.P. (December, January 8\u2013). A Model-Based Road Sign Identification System. Kauai, HI, USA."},{"key":"ref_6","unstructured":"Miura, J., Kanda, T., and Shirai, Y. (October, January 1\u2013). An Active Vision System for Real-Time Traffic Sign Recognition. Dearborn, MI, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Malik, R., Khurshid, J., and Ahmad, S.N. (2007, January 19\u201322). Road Sign Detection and Recognition Using Colour Segmentation, Shape Analysis and Template Matching. Hong Kong, China.","DOI":"10.1109\/ICMLC.2007.4370763"},{"key":"ref_8","unstructured":"Barnes, N., and Zelinsky, A. (June, January 14\u2013). Real-Time Radial Symmetry for Speed Sign Detection. Parma, Italy."},{"key":"ref_9","unstructured":"Zin, T.T., and Hama, H. (October, January 3\u2013). Robust Road Sign Recognition Using Standard Deviation. Washington, DC, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/0262-8856(95)01057-2","article-title":"Robust method for road sign detection and recognition","volume":"14","author":"Piccioli","year":"1996","journal-title":"Image Vis. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1011168302294","article-title":"Recognition, resolution, and complexity of objects subject to affine transformations","volume":"44","author":"Betke","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/TITS.2010.2073466","article-title":"Image segmentation and shape analysis for road-sign detection","volume":"12","author":"Khan","year":"2011","journal-title":"IEEE Trans. Intel. Transp. Syst."},{"key":"ref_13","unstructured":"Khan, J.F., Adhami, R.R., and Bhuiyan, S.M.A. (March, January 5\u2013). Image Segmentation Based Road Sign Detection. Atlanta, GA, USA."},{"key":"ref_14","unstructured":"Gavrila, D., and Philomin, V. (September, January 20\u2013). Real-Time Object Detection for \u2018Smart\u2019 Vehicles. Kerkyra, Greece."},{"key":"ref_15","unstructured":"Kouzani, A.Z. (June, January 13\u2013). Road-Sign Identification Using Ensemble Learning. Istanbul, Turkey."},{"key":"ref_16","unstructured":"Yang, S., and Wang, M. (May, January 23\u2013). Identification of Road Signs Using a New Ridgelet Network. Kobe, Japan."},{"key":"ref_17","unstructured":"Lim, K.H., Ang, L.M., and Seng, K.P. (2008, January 13\u201316). New Hybrid Technique for Traffic Sign Recognition. Bangkok, Thailand."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/41.649946","article-title":"Road traffic sign detection and classification","volume":"44","author":"Moreno","year":"1997","journal-title":"IEEE Trans. Indust. Electron."},{"key":"ref_19","unstructured":"Damavandi, Y.B., and Mohammadi, K. (December, January 1\u2013). Speed Limit Traffic Sign Detection and Recognition. Singapore, Singapore."},{"key":"ref_20","unstructured":"Broggi, A., Cerri, P., Medici, P., Porta, P., and Ghisio, G. (June, January 13\u2013). Real Time Road Signs Recognition. Istanbul, Turkey."},{"key":"ref_21","unstructured":"Bargeton, A., Moutarde, F., Nashashibi, F., and Bradai, B. (June, January 4\u2013). Improving Pan-European Speed-Limit Signs Recognition with a New Global Number Segmentation before Digit Recognition."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chourasia, J.N., and Bajaj, P. (2010, January 19\u201321). Centroid Based Detection Algorithm for Hybrid Traffic Sign Recognition System. Goa, India.","DOI":"10.1109\/ICETET.2010.69"},{"key":"ref_23","unstructured":"Martinovic, A., Glavas, G., Juribasic, M., Sutic, D., and Kalafatic, Z. (2010, January 24\u201328). Real-Time Detection and Recognition of Traffic Signs. Opatija, Croatia."},{"key":"ref_24","unstructured":"Hechri, A., and Mtibaa, A. (March, January 25\u2013). Automatic Detection and Recognition of Road Sign for Driver Assistance System. Medina Yasmine Hammamet, Tunisia."},{"key":"ref_25","unstructured":"Zheng, Y.J., Ritter, W., and Janssen, R. (October, January 24\u2013). An Adaptive System for Traffic Sign Recognition. Paris, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, H.M., Liu, C.L., Liu, K.H., and Huang, S.M. (2003, January 28\u201331). Traffic Sign Recognition in Disturbing Environments. Maebashi, Japan.","DOI":"10.1007\/978-3-540-39592-8_35"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1016\/S0167-8655(00)00078-7","article-title":"Road sign classification using Laplace kernel classifier","volume":"21","author":"Paclik","year":"2000","journal-title":"Patt. Recog. Lett."},{"key":"ref_28","unstructured":"Kressel, U., Lindner, F., Wohler, C., and Linz, A. (September, January 7\u2013). Hypothesis Verification Based on Classification at Unequal Error Rates. Edinburgh, Scotland."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1093\/ietfec\/e88-a.9.2419","article-title":"Traffic sign classification using ring partitioned method","volume":"88-A","author":"Soetedjo","year":"2005","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_30","unstructured":"Gil-Jimenez, P., Gomez-Moreno, H., Siegmann, P., Lafuente-Arroyo, S., and Maldonado-Bascon, S. (June, January 13\u2013). Traffic Sign Shape Classification Based on Support Vector Machines and the FFT of the Signature of Blobs. Istanbul, Turkey."},{"key":"ref_31","unstructured":"Maldonado-Bascon, S., Lafuente-Arroyo, S., Siegmann, P., Gomez-Moreno, H., and Acevedo-Rodriguez, F.J. (June, January 4\u2013). Traffic Sign Recognition System for Inventory Purposes. Eindhoven, The Netherlands."},{"key":"ref_32","unstructured":"Bahlmann, C., Zhu, Y., Visvanathan, R., Pellkofer, M., and Koehler, T. (June, January 6\u2013). A System for Traffic Sign Detection, Tracking, and Recognition Using Color, Shape, and Motion Information. Las Vegas, NV, USA."},{"key":"ref_33","unstructured":"Chen, X., Duan, B., Dong, H., Fu, P., Yuan, H., and Zhao, H. (June, January 3\u2013). A System for Road Sign Detection, Recognition and Tracking Based on Multi-Cues Hybrid. Xi'an, China."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, Y.-S., Fu, M.-Y., and Ma, H.-B. (2010, January 16\u201317). A New Traffic Sign Recognition System with IFRS Detector and MP-SVM Classifier. Wuhan, China.","DOI":"10.1109\/GCIS.2010.63"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Soendoro, D., and Supriana, I. (2011, January 17\u201319). Traffic Sign Recognition with Color-based Method, Shape-arc Estimation and SVM. Bandung, Indonesia.","DOI":"10.1109\/ICEEI.2011.6021584"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.3390\/s120201148","article-title":"Complete vision-based traffic sign recognition supported by an I2V communication system","volume":"12","author":"Garrido","year":"2012","journal-title":"Sensors"},{"key":"ref_37","unstructured":"Zhang, Q., and Kamata, S. (2011, January 13\u201318). Manifold Learning Based on Multi-Feature for Road-Sign Recognition. Tokyo, Japan."},{"key":"ref_38","unstructured":"Sebanja, I., and Megherbi, D.B. (November, January 8\u2013). Automatic Detection and Recognition of Traffic Road Signs for Intelligent Autonomous Unmanned Vehicles for Urban Surveillance and Rescue. Waltham, MA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fatmehsan, Y.R., Ghahari, A., and Zoroofi, R.A. (2010, January 2\u20134). Gabor Wavelet for Road Sign Detection and Recognition Using a Hybrid Classifier. Sharjah, United Arab Emirates.","DOI":"10.1109\/MCIT.2010.5444860"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TITS.2008.2011702","article-title":"Traffic sign recognition using evolutionary adaboost detection and Forest-ECOC classification","volume":"10","author":"Baro","year":"2009","journal-title":"IEEE Trans. Intel. Transp. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fu, M.-Y., and Huang, Y.-S. (2010, January 11\u201314). A Survey of Traffic Sign Recognition. Qingdao, China.","DOI":"10.1109\/ICWAPR.2010.5576425"},{"key":"ref_42","unstructured":"Vislab. Available online: http:\/\/www.vislab.it\/ (accessed on 22 May 2012)."},{"key":"ref_43","unstructured":"IVVI Intelligent Vehicle Base on Visual Information. Available online: http:\/\/www.uc3m.es\/portal\/page\/portal\/dpto_ing_sistemas_automatica\/home\/research_activities\/isl\/intelligent_transportation_systems\/vehicles\/ivvi\/ (accessed on 22 May 2012)."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/9\/12153\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:52:10Z","timestamp":1760219530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/9\/12153"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,9,5]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2012,9]]}},"alternative-id":["s120912153"],"URL":"https:\/\/doi.org\/10.3390\/s120912153","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2012,9,5]]}}}