  Recently, Wi-Fi has caught tremendous attention for its ubiquity, and,
motivated by Wi-Fi's low cost and privacy preservation, researchers have been
putting lots of investigation into its potential on action recognition and even
person identification. In this paper, we offer an comprehensive overview on
these two topics in Wi-Fi. Also, through looking at these two topics from an
unprecedented perspective, we could achieve generality instead of designing
specific ad-hoc features for each scenario. Observing the great resemblance of
Channel State Information (CSI, a fine-grained information captured from the
received Wi-Fi signal) to texture, we proposed a brand-new framework based on
computer vision methods. To minimize the effect of location dependency embedded
in CSI, we propose a novel de-noising method based on Singular Value
Decomposition (SVD) to eliminate the background energy and effectively extract
the channel information of signals reflected by human bodies. From the
experiments conducted, we demonstrate the feasibility and efficacy of the
proposed methods. Also, we conclude factors that would affect the performance
and highlight a few promising issues that require further deliberation.
