  Edge computing and 5G have made it possible to perform analytics closer to
the source of data and achieve super-low latency response times, which is not
possible with centralized cloud deployment. In this paper, we present a novel
fever-screening system, which uses edge machine learning techniques and
leverages private 5G to accurately identify and screen individuals with fever
in real-time. Particularly, we present deep-learning based novel techniques for
fusion and alignment of cross-spectral visual and thermal data streams at the
edge. Our novel Cross-Spectral Generative Adversarial Network (CS-GAN)
synthesizes visual images that have the key, representative object level
features required to uniquely associate objects across visual and thermal
spectrum. Two key features of CS-GAN are a novel, feature-preserving loss
function that results in high-quality pairing of corresponding cross-spectral
objects, and dual bottleneck residual layers with skip connections (a new,
network enhancement) to not only accelerate real-time inference, but to also
speed up convergence during model training at the edge. To the best of our
knowledge, this is the first technique that leverages 5G networks and limited
edge resources to enable real-time feature-level association of objects in
visual and thermal streams (30 ms per full HD frame on an Intel Core i7-8650
4-core, 1.9GHz mobile processor). To the best of our knowledge, this is also
the first system to achieve real-time operation, which has enabled fever
screening of employees and guests in arenas, theme parks, airports and other
critical facilities. By leveraging edge computing and 5G, our fever screening
system is able to achieve 98.5% accuracy and is able to process about 5X more
people when compared to a centralized cloud deployment.
