MTPGait: Multi-person Gait Recognition with Spatio-temporal Information via Millimeter Wave Radar

Abstract

As one of the important methods of identity recognition, gait recognition has a wide range of applications in the fields of new human-computer interaction, smart home, smart office and health monitoring. In this paper, we propose a system for multi-person gait recognition (MTPGait) with spatio-temporal information via millimeter wave radar. We specially design a neural network that can extract multi-scale spatio-temporal features along space and time dimensions of 3D point cloud concisely and efficiently. In addition, we construct and release a millimeter wave radar 3D point cloud data set, which consists of 960-minute gait data of 25 volunteers. The experimental results show that MTPGait is able to achieve 96.7% recognition accuracy in a single-person scene on random routes, and 90.2 % recognition accuracy when two people coexist, while the accuracy of the existing methods can not reach 90 % in either scenario.