Multiple people continuous tracking and identification using millimeter-wave radar

Abstract

Automatic people tracking and identification have a lot of application prospects in access control, intelligent monitoring, personalized service, etc. Although the primary sensors used now are cameras, they are challenging to cope with low light conditions, adverse weather conditions, and clothing changes. The privacy risks brought by cameras cannot be ignored with people's increasing awareness of privacy. In this paper, we use a commercial millimeter-wave radar to track and identify multiple people indoors. The mmWave radar can "see" objects even in the dark and protects people's private information. We propose PPMM mechanism to solve the problem of tracking multiple people walking at close distances. What's more, we design transformer for mmWave radar pointclouds (TMP) based on transformer architecture. Finally, we evaluate our model and demonstrate the results on our dataset, which involved 6 people. Our method can track up to 3 people simultaneously. We achieve the best identification accuracy of 86.34% overall different numbers of people scenarios, and the accuracy of single, two, and three people scenarios are 87.93%, 87.00%, and 64.98%.