Name
Abstract | ||
|---|---|---|
Three-dimensional (3D) driver pose estimation is a promising and challenging problem for computer-human interaction. Recently convolutional neural networks have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this study, the proposed method is based on two types of inputs, infrared image and point cloud... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-cvi.2019.0089 | IET Computer Vision |
Keywords | Field | DocType |
cameras,driver information systems,feature extraction,pose estimation | Computer vision,Computer science,Pose,Artificial intelligence | Conference |
Volume | Issue | ISSN |
14 | 3 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhijie Yao | 1 | 0 | 0.34 |
| Yazhou Liu | 2 | 10 | 3.18 |
| Zexuan Ji | 3 | 459 | 26.03 |
| Quansen Sun | 4 | 1222 | 83.09 |
| Pongsak Lasang | 5 | 16 | 5.29 |
| Sheng Mei Shen | 6 | 131 | 13.13 |