Vision-Based Online Localization and Trajectory Smoothing for Fixed-Wing UAV Tracking a Moving Target
Abstract
Using unmanned aerial vehicle (UAV) to estimate and predict the position and motion of the ground target has been widely focused on in many computer vision tasks. This work aims to address the development of a vision-based ground target localization and estimation for a fixed-wing UAV. Limited by the lightweight onboard processor, it is conflicting with the need for online onboard operation and the computing resource limitation of the platform. In this paper, we develop a practical approach to recover dynamic targets based on extended Kalman filter (EKF) for localization and locally weighted regression for trajectory smoothing. Our methods run online in real time with the only data up to the current timestep. The flight experiment results show the effective tracking and localization of the ground moving target.
Cite
Text
Zhou et al. "Vision-Based Online Localization and Trajectory Smoothing for Fixed-Wing UAV Tracking a Moving Target." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00024Markdown
[Zhou et al. "Vision-Based Online Localization and Trajectory Smoothing for Fixed-Wing UAV Tracking a Moving Target." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/zhou2019iccvw-visionbased/) doi:10.1109/ICCVW.2019.00024BibTeX
@inproceedings{zhou2019iccvw-visionbased,
title = {{Vision-Based Online Localization and Trajectory Smoothing for Fixed-Wing UAV Tracking a Moving Target}},
author = {Zhou, Yong and Tang, Dengqing and Zhou, Han and Xiang, Xiaojia and Hu, Tianjiang},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {153-160},
doi = {10.1109/ICCVW.2019.00024},
url = {https://mlanthology.org/iccvw/2019/zhou2019iccvw-visionbased/}
}