Name
Abstract | ||
|---|---|---|
AbstractThis article proposes the design of a novel hardware embedded system used for automatic real-time road sign recognition. The algorithm used was implemented in two main steps. The first step, which detects the road signs, is performed by the maximally stable extremal region method on HSV color space. The second step enables the recognition of the detected signs by using the oriented fast and rotated brief features method. The novelty of the embedded hardware system, on an ARM processor, leads to a real-time implementation of the ADAS applications. The proposed system was tested on the Belgium Traffic Sign Detection and Recognition Benchmark and on the German Traffic Signs Datasets. The proposed approach attained a high detection and recognition rate with real-world situations. The achieved results are acceptable when compared to state-of-the-art systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.4018/IJAMC.2020040104 | Periodicals |
Keywords | Field | DocType |
Advanced Driver Assistance Systems (ADAS), ARM processor, Detection, Raspberry Pi, Real-Time, Recognition, Road Traffic Sign | ARM architecture,Real-time computing,Real-time operating system,Traffic sign recognition,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 2 | 1947-8283 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hassene Faiedh | 1 | 21 | 3.25 |
| Wajdi Farhat | 2 | 0 | 0.34 |
| Sabrine Hamdi | 3 | 0 | 0.34 |
| Chokri Souani | 4 | 41 | 8.75 |