  In this paper, we propose a radio-assisted human detection framework by
incorporating radio information into the state-of-the-art detection methods,
including anchor-based onestage detectors and two-stage detectors. We extract
the radio localization and identifer information from the radio signals to
assist the human detection, due to which the problem of false positives and
false negatives can be greatly alleviated. For both detectors, we use the
confidence score revision based on the radio localization to improve the
detection performance. For two-stage detection methods, we propose to utilize
the region proposals generated from radio localization rather than relying on
region proposal network (RPN). Moreover, with the radio identifier information,
a non-max suppression method with the radio localization constraint has also
been proposed to further suppress the false detections and reduce miss
detections. Experiments on the simulative Microsoft COCO dataset and Caltech
pedestrian datasets show that the mean average precision (mAP) and the miss
rate of the state-of-the-art detection methods can be improved with the aid of
radio information. Finally, we conduct experiments in real-world scenarios to
demonstrate the feasibility of our proposed method in practice.
