E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles

TL;DR

E^2VTS is an energy-efficient video text spotting system for UAVs that combines optimized training, multi-stage image processing, and model pruning to balance performance and energy consumption.

Contribution

The paper introduces a novel energy-efficient video text spotting approach tailored for UAVs, including a new training strategy, multi-stage processing, and deployment optimizations.

Findings

Outperforms previous methods in energy efficiency and accuracy.

Effective pruning and quantization enable deployment on Raspberry Pi.

Empirical validation on UAV-captured video datasets.

Abstract

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.