E2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles
| Autor: | Jiayi Shen, Zhangyang Wang, Jianchao Tan, Zhenyu Hu, Zhenyu Wu, Xiangru Lian, Pengcheng Pi, Yunhe Xue, Ji Liu |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Image processor Real-time computing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Energy consumption Spotting Raspberry pi Sampling (signal processing) Redundancy (engineering) Artificial intelligence Routing (electronic design automation) business Efficient energy use |
| Zdroj: | CVPR Workshops |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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