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.

翻译：夜间人体动作识别技术在监控、运动控制和人机交互等领域需求巨大，正受到越来越多的关注。然而，由于图像增强方法的局限性和低光照视频数据集（例如标注成本）的限制，现有方法面临一些问题。一些基于视频的方法在特定数据集上效果显著且高效，但无法推广到大多数情况；而其他使用多传感器的方法则严重依赖先验知识来处理视频流中的噪声特性。本文提出了一种基于深度多输入网络的动作识别方法。此外，我们提出了一种独立的伽马强度校正（Ind-GIC）方法来增强弱光照视频，即为每一帧生成一个伽马值以提高增强性能。为了证明我们方法的有效性，我们进行了评估，并将我们的方法与现有方法进行了比较。实验结果表明，我们的模型在ARID数据集上取得了较高的准确率。