Sensors, Volume 24, Issue 18
2024 September-2 - 299 articles
Cover Story: In public security and surveillance, efficacy and privacy protection measures must be continually evaluated. Our work deploys sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy. This is achieved by coupling mmWave radar and depth camera technology with a novel neural network architecture that processes radar signals using convolutional Long Short-Term Memory blocks and depth signals using convolutional operations. We demonstrate the detection of presence and 3D location of concealed metal objects, achieving accuracies of up to 95% using a technique that is robust to multiple persons. This work provides a demonstration of the potential for cost-effective and portable sensor fusion with strong opportunities for further development. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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