Name
Title | ||
|---|---|---|
Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds. |
Abstract | ||
|---|---|---|
Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11121453 | REMOTE SENSING |
Keywords | Field | DocType |
traffic sign,visibility,recognizability,mobile laser scanner,point clouds | Computer vision,Remote sensing,Lidar,Artificial intelligence,Traffic sign,Geology,Point cloud | Journal |
Volume | Issue | Citations |
11 | 12 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shanxin Zhang | 1 | 0 | 0.68 |
| Cheng Wang | 2 | 118 | 29.56 |
| Lili Lin | 3 | 2 | 0.72 |
| Chenglu Wen | 4 | 121 | 19.17 |
| Chen-Hui Yang | 5 | 50 | 10.88 |
| Zhemin Zhang | 6 | 0 | 0.34 |
| Jonathan Li | 7 | 798 | 119.18 |