DyLiteRADHAR: DYNAMIC LIGHTWEIGHT SLOWFAST NETWORK FOR HUMAN ACTIVITY RECOGNITION USING MMWAVE RADAR

Millimeter-wave radar based human activity recognition (RADHAR) exhibits remarkable prospects in the field of device-free sensing. However, most existing RADHAR systems only focus on performance improvement, failing to simultaneously lighten the network parameters. In this paper, we propose a dynamic lightweight SlowFast network named DyLiteRADHAR, which can efficiently extract spatial-temporal features and largely reduce the resource consumption for human activity recognition. Specifically, we design triple-view signal maps (TRIview) as the input by successively concatenating the range-velocity, range-azimuth and range-elevation matrices. Then dynamic lightweight network is presented to learn discriminative representations which integrates dynamic convolution and lightweight shuffle net structure into the SlowFast framework. Experimental results demonstrate that the proposed approach DyLiteRADHAR is able to achieve superiority performance with limited computation complexity.