E2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles
Abstract
Unmanned Aerial Vehicles (UAVs) based video text spot-ting has been extensively used in civil and military domains. UAV’s limited battery capacity motivates us to develop an energy-efficient video text spotting solution. In this paper, we first revisit RCNN’s crop & resize training strategy and empirically find that it outperforms aligned RoI sampling on a real-world video text dataset captured by UAV. To re-duce energy consumption, we further propose a multi-stage image processor that takes videos’ redundancy, continuity, and mixed degradation into account. The model is pruned and quantized before deployed on Raspberry Pi. Our pro-posed energy-efficient video text spotting solution, dubbed as E2V T S, outperforms all previous methods by achieving a competitive tradeoff between energy efficiency and performance. All our codes and pre-trained models are available at https://github.com/wuzhenyusjtu/LPCVC20-VideoTextSpotting.
Cite
Text
Hu et al. "E2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00101Markdown
[Hu et al. "E2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/hu2021cvprw-e2vts/) doi:10.1109/CVPRW53098.2021.00101BibTeX
@inproceedings{hu2021cvprw-e2vts,
title = {{E2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles}},
author = {Hu, Zhenyu and Pi, Pengcheng and Wu, Zhenyu and Xue, Yunhe and Shen, Jiayi and Tan, Jianchao and Lian, Xiangru and Wang, Zhangyang and Liu, Ji},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2021},
pages = {905-913},
doi = {10.1109/CVPRW53098.2021.00101},
url = {https://mlanthology.org/cvprw/2021/hu2021cvprw-e2vts/}
}