Name
Abstract | ||
|---|---|---|
Automatic traffic sign recognition enhances driver interactivity while driving. It improves the vigilance of the driver by alarming-him/her of signs that he/she may not perceive. In this paper, an embedded real-time system for automatic traffic sign recognition is proposed. The segmentation task of an acquired scene is processed in the HSV color space. The recognition process is performed by using the Oriented fast-and-Rotated Brief features. The developed algorithm is implemented on a ZedBoard hardware platform. The detection rate reaches the value of 97.39%. The recognition rate is equal to 95.53%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12652-017-0673-3 | J. Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Advanced driver assistance system, Traffic sign recognition, Embedded system, Real-time, Intelligent transport system, ZedBoard | Interactivity,HSL and HSV,Computer vision,Road sign recognition,Computer science,Segmentation,Vigilance (psychology),Traffic sign recognition,Artificial intelligence | Journal |
Volume | Issue | ISSN |
10 | 2 | 1868-5145 |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wajdi Farhat | 1 | 3 | 0.76 |
| Souhir Sghaier | 2 | 3 | 0.76 |
| Hassene Faiedh | 3 | 21 | 3.25 |
| Chokri Souani | 4 | 41 | 8.75 |