Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition

TL;DR

Wi-CBR introduces a salient-aware adaptive WiFi sensing framework that leverages dual-branch self-attention and saliency guidance to improve cross-domain behavior recognition by effectively capturing dynamic and kinematic features.

Contribution

The paper proposes a novel Wi-CBR model with dual-branch self-attention and saliency guidance modules for enhanced cross-domain behavior recognition using WiFi signals.

Findings

Outperforms existing methods on Widar3.0 and XRF55 datasets

Effective in both in-domain and cross-domain scenarios

Improves feature extraction by combining phase and DFS signals

Abstract

The challenge in WiFi-based cross-domain Behavior Recognition lies in the significant interference of domain-specific signals on gesture variation. However, previous methods alleviate this interference by mapping the phase from multiple domains into a common feature space. If the Doppler Frequency Shift (DFS) signal is used to dynamically supplement the phase features to achieve better generalization, it enables the model to not only explore a wider feature space but also to avoid potential degradation of gesture semantic information. Specifically, we propose a novel Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition (Wi-CBR), which constructs a dual-branch self-attention module that captures temporal features from phase information reflecting dynamic path length variations while extracting kinematic features from DFS correlated with motion velocity. Moreover, we design a Saliency Guidance Module that employs group attention mechanisms to mine critical activity features and utilizes gating mechanisms to optimize information entropy, facilitating feature fusion and enabling effective interaction between salient and non-salient behavioral characteristics. Extensive experiments on two large-scale public datasets (Widar3.0 and XRF55) demonstrate the superior performance of our method in both in-domain and cross-domain scenarios.