  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.
