Name
Title | ||
|---|---|---|
Automatic Recognition of Traffic Signs with 3D Distance Estimation for Intelligent Vehicles. |
Abstract | ||
|---|---|---|
Traffic sign recognition TSR systems within the Advanced Driving Assistance Systems ADAS has attract many researchers as it plays a vital role to ensure road and transportation safety. In this paper, the authors introduce a new method for the automatic recognition and distance estimation of traffic signs using hybrid 2D-3D approach. The proposed method performs on two steps: Traffic signs recognition and distance estimation. The traffic signs recognition step aims to detect, classify and track traffic signs. Regarding the distance estimation step, it seeks to compute the depth distance of the traffic sign so as to estimate the distance between the recognized traffic sign and the vehicle carrying the camera. Many series of experiments were carried out on GTSBD and KITTI Stereo 2015 databases to evaluate the performance of the authors' method. The obtained results have shown that the proposed method achieves good performance in challenging scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJSI.2017040105 | IJSI |
Keywords | DocType | Volume |
Dense Matching, Distance Computing, Driver Assistance System, SVM Classifier, Traffic Sign Recognition | Journal | 5 |
Issue | ISSN | Citations |
2 | 2166-7160 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadra Ben Romdhane | 1 | 0 | 0.34 |
| Hazar Mliki | 2 | 0 | 0.34 |
| Mohamed Hammami | 3 | 181 | 30.54 |