Next Article in Journal
The Electromechanical Behavior of a Micro-Ring Driven by Traveling Electrostatic Force
Previous Article in Journal
Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor
Sensors 2012, 12(2), 1148-1169; doi:10.3390/s120201148
Article

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

1,* , 1
,
2
,
1
,
1
 and
2
1 Electronics Department, Polytechnic School, University of Alcalá, Madrid 28871, Spain 2 Computer Engineering Department, Polytechnic School, University of Alcalá, Madrid 28871, Spain
* Author to whom correspondence should be addressed.
Received: 2 December 2011 / Revised: 12 January 2012 / Accepted: 20 January 2012 / Published: 30 January 2012
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [5037 KB, uploaded 21 June 2014]   |   Browse Figures
SciFeed

Abstract

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.
Keywords: traffic sign recognition; advanced driver assistance systems; I2V; computer vision traffic sign recognition; advanced driver assistance systems; I2V; computer vision
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
RIS
MDPI and ACS Style

García-Garrido, M.A.; Ocaña, M.; Llorca, D.F.; Arroyo, E.; Pozuelo, J.; Gavilán, M. Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System. Sensors 2012, 12, 1148-1169.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert