Name
Title | ||
|---|---|---|
Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs |
Abstract | ||
|---|---|---|
We present a new modular traffic signs recognition system, successfully applied to both American and European speed limit signs. Our sign detection step is based only on shape-detection (rectangles or circle s). This enables it to work on grayscale images, contrary to most European competitors, which eases robustness to illumination conditions (notably night operation). Speed sign candidates are classified (or rejected) by segmenting potential digits inside them (which is rather original and has several advantages), and then applying a neural digit recognition. The global detection rate is ~90% for both (standard) U.S. and E.U. speed signs, with a misclassification rate <1%, and no validated false alarm in >150 minutes of video. The system processes in real-time ~20 frames/s on a sta ndard high-end laptop. |
Year | Venue | Keywords |
|---|---|---|
2009 | Clinical Orthopaedics and Related Research | intelligent transportation systems,image processing |
DocType | Volume | ISSN |
Journal | abs/0910.1 | 14th World congress on Intelligent Transportation Systems
(ITS'2007), Beijing : China (2007) |
Citations | PageRank | References |
2 | 0.57 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabien Moutarde | 1 | 54 | 15.26 |
| Alexandre Bargeton | 2 | 4 | 1.34 |
| Anne Herbin | 3 | 2 | 0.57 |
| Lowik Chanussot | 4 | 2 | 0.57 |