  In the field of detection and ranging, multiple complementary sensing
modalities may be used to enrich the information obtained from a dynamic scene.
One application of this sensor fusion is in public security and surveillance,
whose efficacy and privacy protection measures must be continually evaluated.
We present a novel deployment of sensor fusion for the discrete detection of
concealed metal objects on persons whilst preserving their privacy. This is
achieved by coupling off-the-shelf mmWave radar and depth camera technology
with a novel neural network architecture that processes the radar signals using
convolutional Long Short-term Memory (LSTM) blocks and the depth signal, using
convolutional operations. The combined latent features are then magnified using
a deep feature magnification to learn cross-modality dependencies in the data.
We further propose a decoder, based on the feature extraction and embedding
block, to learn an efficient upsampling of the latent space to learn the
location of the concealed object in the spatial domain through radar feature
guidance. We demonstrate the detection of presence and inference of 3D location
of concealed metal objects with an accuracy 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.
