Deep convolutional neural networks (ConvNets) have been recently shown to attain state-of-the-art performance for action recognition on standard-resolution videos. However, less attention has been paid to recognition performance at extremely low resolutions (eLR) (e.g., 16 x 12 pixels). Reliable action recognition using eLR cameras would address privacy concerns in various application environments such as private homes, hospitals, nursing/rehabilitation facilities, etc. In this paper, we propose a semi-coupled filter-sharing network that leverages high resolution (HR) videos during training in order to assist an eLR ConvNet. We also study methods for fusing spatial and temporal ConvNets customized for eLR videos in order to take advantage of appearance and motion information. Our method outperforms state-of-the-art methods at extremely low resolutions on IXMAS (93.7%) and HMDB (29.2%) datasets.

本文提出了一种半耦合滤波器共享网络，通过在训练期间利用高分辨率视频来支持低分辨率视频的ConvNet，同时研究了用于融合针对eLR视频定制的空间和时间ConvNets的方法，这种方法在IXMAS（93.7％）和HMDB（29.2％）数据集上优于现有技术。

极低分辨率下行为识别的半耦合双流融合卷积神经网络