Technologies of human action recognition in the dark are gaining more and
more attention as huge demand in surveillance, motion control and
human-computer interaction. However, because of limitation in image enhancement
method and low-lighting video datasets, e.g. labeling cost, existing methods
meet some problems. Some video-based approached are effect and efficient in
specific datasets but cannot generalize to most cases while others methods
using multiple sensors rely heavily to prior knowledge to deal with noisy
nature from video stream. In this paper, we proposes action recognition method
using deep multi-input network. Furthermore, we proposed a Independent Gamma
Intensity Corretion (Ind-GIC) to enhance poor-illumination video, generating
one gamma for one frame to increase enhancement performance. To prove our
method is effective, there is some evaluation and comparison between our method
and existing methods. Experimental results show that our model achieves high
accuracy in on ARID dataset.

在这篇论文中，我们提出了一种使用深度多输入网络的行为识别方法，并且还提出了一种独立伽马强度校正（Ind-GIC）来增强贫光照视频，通过生成一个伽马值来提高增强性能。通过与现有方法的评估和比较，实验结果表明我们的模型在 ARID 数据集上取得了高准确度。