Deep Learning based Human Activity Classification in Radar Micro-Doppler Image

A convolutional neural network (CNN) based deep learning (DL) approach to classify human activities in micro-Doppler spectrogram of radar is investigated. MOCAP dataset, from Carnegie Mellon University, is used for spectrogram simulation. Seven activities are classified with the proposed CNN network. Our network outperforms several previously published DL-based approaches. To understand the network's impact on classification performance, we investigate some key parameters of the proposed network. Experiment result demonstrates that a deeper network does not necessarily result in a higher accuracy. We also examine the network size and the number of output feature maps to find out their impact on the result.