We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods
This work addresses privacy-preserving human activity monitoring using ubiquitous Wi-Fi, but it appears incremental as it builds on existing Wi-Fi sensing research by adapting vision-based techniques.
The paper tackles action recognition and person identification using Wi-Fi signals by proposing a novel framework that applies computer vision methods to Channel State Information (CSI), treating it like texture, and introduces a de-noising method based on Singular Value Decomposition (SVD) to reduce location dependency. The experiments demonstrate the feasibility and efficacy of the proposed methods, though no concrete performance numbers are provided in the abstract.
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.