| Paper ID | IVMSP-31.4 |
| Paper Title |
VEHICLE 3D LOCALIZATION IN ROAD SCENES VIA A MONOCULAR MOVING CAMERA |
| Authors |
Yanting Zhang, Donghua University, China; Aotian Zheng, University of Washington, United States; Ke Han, Fudan University, China; Yizhou Wang, University of Washington, United States; Jenq-Neng Hwang, University of Washinton, United States |
| Session | IVMSP-31: Applications 3 |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Knowing the 3D locations of the surrounding vehicles is of vital importance in autonomous driving scenarios. It can be pretty challenging to make an accurate estimation from a monocular moving camera. In this paper, we present an effective vehicle 3D localization method, that utilizes 2D keypoints predicted from a trained CNN to model the vehicles' structure, from which the ground points are further inferred. An adaptive ground plane estimation method is exploited under the monocular camera for 3D geometric back-projection. Benefiting from tracking, we also take into account temporal information of the same object to ensure the trajectory consistency. Viewpoint and size knowledge are also considered for refinement. The evaluation on the KITTI benchmark for on-road vehicles shows the effectiveness of our proposed approach with promising 3D localization results. |
