  In radar activity recognition, 2D signal representations such as spectrogram,
cepstrum and cadence velocity diagram are often utilized, while range
information is often neglected. In this work, we propose to utilize the 3D
time-range-Doppler (TRD) representation, and design a 3D Orthogonally-Projected
EfficientNet (3D-OPEN) to effectively capture the discriminant information
embedded in the 3D TRD cubes for accurate classification. The proposed model
aggregates the discriminant information from three orthogonal planes projected
from the 3D feature space. It alleviates the difficulty of 3D CNNs in
exploiting sparse semantic abstractions directly from the high-dimensional 3D
representation. The proposed method is evaluated on the Millimeter-Wave Radar
Walking Dataset. It significantly and consistently outperforms the
state-of-the-art methods for radar activity recognition.
