E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles
This work addresses energy consumption issues in UAV-based video text spotting for civil and military applications, presenting an incremental improvement.
The paper tackles the problem of energy-efficient video text spotting from UAVs, proposing a solution that achieves a competitive tradeoff between energy efficiency and performance, with all codes and models made available.
Unmanned Aerial Vehicles (UAVs) based video text spotting 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 reduce energy consumption, we further propose a multi-stage image processor that takes videos' redundancy, continuity, and mixed degradation into account. Lastly, the model is pruned and quantized before deployed on Raspberry Pi. Our proposed energy-efficient video text spotting solution, dubbed as E^2VTS, 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.