Name
Abstract | ||
|---|---|---|
High-resolution micro traffic data are important to traffic safety and efficiency analysis. In this study, a roadside LiDAR sensor is used to collect 3D point clouds of surrounding objects. An automatic background construction and object detection method is proposed on the basis of the operation principle of the LiDAR sensor. In the algorithm, the discrete horizontal and vertical angular values can be considered as coordinates of pixels in digital images, and the farthest and mean distance of each azimuth are used to construct the background dataset. Then, vehicle and pedestrian points are extracted on the basis of the distance difference of each point with the same angular value between the target frame dataset and the background dataset. A density-based spatial clustering method is employed to group points into clusters and identify vehicles and pedestrians automatically. Finally, the performance of the algorithm is tested for roadside LiDAR data preprocessing under different traffic conditions. Our algorithm can perform well with high accuracy. Experimental results demonstrate that our algorithm can achieve a larger detection range and exhibit lower time complexity in comparison with a previously proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TITS.2019.2936498 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Laser radar,Laser beams,Three-dimensional displays,Measurement by laser beam,Sensor phenomena and characterization,Digital images | Journal | 21 |
Issue | ISSN | Citations |
10 | 1524-9050 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyao Zhang | 1 | 1 | 0.71 |
| Jianying Zheng | 2 | 8 | 1.97 |
| Hao Xu | 3 | 7 | 1.26 |
| Xiang Wang | 4 | 1 | 0.71 |
| Xueliang Fan | 5 | 2 | 0.73 |
| Rong Chen | 6 | 55 | 10.48 |