Real-time People Tracking and Identification from Sparse mm-Wave Radar Point-clouds

TL;DR

This paper introduces a low-complexity, real-time system for tracking and identifying multiple people using sparse mm-wave radar point-clouds, combining extended object tracking and deep learning for high accuracy and efficiency.

Contribution

It presents a novel end-to-end approach integrating an extended object tracking Kalman filter with a deep learning classifier for real-time multi-subject identification from radar data.

Findings

Achieves 91.62% accuracy in identifying three subjects

Operates at 15 frames per second on NVIDIA Jetson platform

Reduces computational complexity compared to existing methods

Abstract

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.