Kavli Affiliate: Xiang Zhang
| First 5 Authors: Ruobei Zhang, Ruobei Zhang, , ,
| Summary:
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, enabling model to not only explore a wider feature space
but also 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 spatial 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 behavior 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.
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