2021/05/24 by Jacopo Pegoraro, Pegoraro, Jacopo, Michele Rossi +1 · 5 citations
Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Gait Recognition and Analysis #Indoor and Outdoor Localization Technologies #Radar Systems and Signal Processing #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.11368
17 pages, 17 figures, 6 tables
arxiv created 2021/05/24 · openalex publication_date 2021/05/24 · arxiv updated 2021/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.