An Accurate Device-Free Action Recognition System Using Two-Stream Network

Abstract

With the popularization of Wi-Fi signals and urgent demands for passive human action recognition, wireless sensing based activity recognition has been a hot topic in recent years. Most existing researches rely on traditional hand-crafted features, and limited work focuses on how to effectively extract deep features with spatial-temporal information. In this paper, we develop an accurate device-free action recognition system utilizing a Commodity Off-The-Shelf (COTS) router and propose a novel deep learning framework (termed two-stream network) mining spatial-temporal cues in channel state information (CSI). Specifically, an entire action sample is segmented into a series of coherent sub-activity clips. Then we try to capture the complementary features on appearance from the original CSI clips and motion between CSI frames. The spatial and temporal information are processed with separate networks which are then integrated for the final recognition task. The extensive experiments are implemented on the data collected from two indoor environments, respectively reaching 97.6% and 96.9% recognition accuracies.