  Mm-wave radars have recently gathered significant attention as a means to
track human movement and identify subjects from their gait characteristics. A
widely adopted method to perform the identification is the extraction of the
micro-Doppler signature of the targets, which is computationally demanding in
case of co-existing multiple targets within the monitored physical space. Such
computational complexity is the main problem of state-of-the-art approaches,
and makes them inapt for real-time use. In this work, we present an end-to-end,
low-complexity but highly accurate method to track and identify multiple
subjects in real-time using the sparse point-cloud sequences obtained from a
low-cost mm-wave radar. Our proposed system features an extended object
tracking Kalman filter, used to estimate the position, shape and extension of
the subjects, which is integrated with a novel deep learning classifier,
specifically tailored for effective feature extraction and fast inference on
radar point-clouds. The proposed method is thoroughly evaluated on an
edge-computing platform from NVIDIA (Jetson series), obtaining greatly reduced
execution times (reduced complexity) against the best approaches from the
literature. Specifically, it achieves accuracies as high as 91.62%, operating
at 15 frames per seconds, in identifying three subjects that concurrently and
freely move in an unseen indoor environment, among a group of eight.
