Radio-Assisted Human Detection

TL;DR

This paper introduces a radio-assisted human detection framework that integrates radio signals with existing detection methods to reduce false positives and negatives, improving accuracy in both simulated and real-world scenarios.

Contribution

It presents a novel approach that incorporates radio localization and identifier information into state-of-the-art detectors, enhancing detection performance and robustness.

Findings

Improved mAP and reduced miss rate on COCO and Caltech datasets.

Enhanced detection accuracy in real-world scenarios.

Effective suppression of false detections using radio localization constraints.

Abstract

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.