Radio-Assisted Human Detection
Chengrun Qiu, Dongheng Zhang, Yang Hu, Houqiang Li, Qibin Sun, Yan, Chen

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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Anomaly Detection Techniques and Applications · Indoor and Outdoor Localization Technologies
