Psychology

Video tracking

Tracking system

Artificial intelligence

Kalman filter

Telecommunications

Pedagogy

Point cloud

Biology

Radar tracker

Computer science

Extremely high frequency

Botany

Tracking (education)

Video processing

Real-time computing

Identification (biology)

Key (lock)

Computer vision

Computer security

Radar

The key to offering personalised 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 (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Unlike competing techniques based on WiFi Channel State Information (CSI), it is capable of tracking and identifying multiple people simultaneously. Using a lowcost, 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. We evaluate and demonstrate our system across a variety of scenarios, showing median position errors of 0.16 m and identification accuracy of 89% for 12 people.

mID: Tracking and Identifying People with Millimeter Wave Radar