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

arXiv: https://arxiv.org/abs/2105.11368 · HTML: https://mlsanity.com/paper/2105.11368/

## Key facts

- Cited by 6 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 May 2021.

## Built on by (most cited first, 6 of 6)

- [MiliPoint: A Point Cloud Dataset for mmWave Radar](https://mlsanity.com/paper/2309.13425/) (2023)
- [A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications](https://mlsanity.com/paper/2112.05593/) (2021)
- [MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing](https://mlsanity.com/paper/2110.03979/) (2021)
- [Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds](https://mlsanity.com/paper/2503.07435/) (2025)
- [Enhanced Sparse Point Cloud Data Processing for Privacy-aware Human Action Recognition](https://mlsanity.com/paper/2508.10469/) (2025)
- [mmFlux: Crowd Flow Analytics with Commodity mmWave MIMO Radar](https://mlsanity.com/paper/2507.07331/) (2025)

## Abstract (arXiv)

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.