Name
Title | ||
|---|---|---|
Multi-nation and Multi-norm License Plates Detection in Real Traffic Surveillance Environment Using Deep Learning. |
Abstract | ||
|---|---|---|
This paper aims to highlight the problems of license plate detection in real traffic surveillance environment. We notice that existing systems require strong assumptions on license plate norm and environment. We propose a novel solution based on deep learning using self-taught features to localize multi-nation and multi-norm license plates under real road conditions such poor illumination, complex background and several positions. Our method is insensitive to illumination day, night, sunrise, sunset,..., translation and poses. Despite the low resolution of images collected from real road surveillance environment, a series of experiments shows interesting results and the fastest time processing comparing with traditional algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46672-9_52 | ICONIP |
Keywords | Field | DocType |
License plate detection,Deep learning,Multi-nation,Multi-norm,Real road surveillance,Low resolution,Poor illumination | Computer vision,Computer science,Norm (social),Notice,Artificial intelligence,Deep learning,License | Conference |
Volume | ISSN | Citations |
9948 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amira Naimi | 1 | 1 | 0.36 |
| Yousri Kessentini | 2 | 100 | 15.39 |
| Mohamed Hammami | 3 | 181 | 30.54 |