Wolfe: Wifi Based Object Recognition Framework Using Multiple Features

Existing work on stationary object recognition using WiFi CSI (Channel State Information) only leverages a single feature such as profile, category, etc. However, in many situations, objects have more than one feature. Multiple features can better reflect characteristics of objects and make them easier to recognize. This paper takes the first step toward multiple feature recognition using WiFi CSI. We propose WOLFE, a WiFi based object recognition framework using multiple features. Our framework matches features and labels by decoding CSI data and multi-label matrices into Gaussian latent spaces and aligning their distributions. By resampling in this Gaussian latent space, we can restore the corresponding label information from the samples and thereby making recognition. Our framework uses different pipelines during training and inference stages to achieve end-to-end recognition. In our experiments, we collect two indoor small-scale static object datasets. WOLFE achieves the recognition accuracy as high as 84.6% and 82.87% respectively. We also consider the scenarios of multiple objects and cross-domain to verify the universality of our framework. The accuracy rate in the multiple objects scenario is over 79%, and in the cross-domain experiment is around 80%, which is less than 4% decrease from the rates in their origin domains.