DRNet: Efficient Few-Shot Learning Model for Regenerating Restricted-Access Area Maps

WiFi and cellular networks have become essential components of our modern lives. On the other hand, these connectivity technologies also enable many radio localization techniques and even user monitoring that offer convenience and valuables, such as object tracking in augmented reality or patient tracking in emergency calls. This paper describes a novel approach for recreating a restricted-access architectural drawing that takes advantage of passive radio localization on multiple users. Initially, we produce data points in an empty building drawing from the data points provided by our built-in signal-to-image-based localization. Next, we propose a few-shot drawing reconstruction model, DRNet, for regenerating the raw structure based on collected data points from localization. Finally, we combine the time-series monitoring data to predict the functionality of the rooms. The assessment findings indicate that the precision of accessible area regeneration can reach a maximum of 83.4%. The methodology used in this study exhibits versatility in its applicability to a wide range of prospective applications. For instance, law enforcement authorities possess the capacity to effectively ascertain the ingress/egress pathways or accessible locations for potential intrusion during a narcotics enforcement operation. Another scenario involves the disclosure of frequently visited locations by criminals or elucidating the purposes served by certain areas inside a restricted-access facility.