Human tracking and identification through a millimeter wave radar

Peijun Zhao, Chris Xiaoxuan Lu, Jianan Wang, Changhao Chen, Wei Wang, Niki Trigoni, Andrew Markham

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The key to offering personalized services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID11Parts of this work have been previously published in 2019 IEEE 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) Zhao et. al. (2019)[1].) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Using a low-cost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users and show how to detect intruders. We evaluate and demonstrate our system, showing median position errors of 0.16 m, identification accuracy of 89% and intruder detection accuracy of 73% for 12 insiders. By increasing observation time from 2 s to 7 s, identification accuracy rises to 99%.
Original languageEnglish
Article number102475
Number of pages11
JournalAd Hoc Networks
Early online date11 Mar 2021
Publication statusPublished - 19 Mar 2021

Keywords / Materials (for Non-textual outputs)

  • Millimeter wave radar
  • Tracking
  • Identification
  • Intruder detection


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