Indoor human action recognition based on millimeter-wave radar micro-doppler signature

Abstract

Considering that millimeter-wave radar lacks sufficient data to support Transfer Learning (TL) and Human Action Recognition (HAR), we propose a Heterogeneous Multi-source Transfer Learning (HMTL) method and a data selection algorithm based on Categorical Probability to obtain Hash Coding (CPHC). CPHC is utilized to select data from multi-source datasets in the same domain for downscaling and matching to ensure the similarity between the selected data features and the target task features to obtain better performance on the target task. The experimental results show that the CPHC can downscale data more efficiently than the traditional algorithm. HMTL can effectively improve the classification accuracy of the network to the state-of-the-art (SOAT) level. RPME addresses that the radar Micro-Doppler Signature (MDS) is flooded by noise due to the cluttered indoor environment, making the MDS more obvious and thus improving the classification accuracy.