Soft Temporal Alignment Distillation for Camera-to-mmWave Radar Human Activity Recognition

Abstract

Millimeter-wave (mmWave) radar provides privacy?preserving human activity recognition (HAR), but sparse point clouds limit its representation of body structure and motion. We propose a cross-modal knowledge distillation framework in which a teacher trained on camera-derived two-dimensional (2D) human keypoints supervises a radar-only student. The stu?dent combines PointNet-based point-set encoding, bidirectional temporal modeling, segment-level aggregation, and lightweight shared latent-channel scaling. The teacher retains contextualized states at every time step, whereas the student summarizes the radar sequence into a small number of segments, making fixed timestamp correspondence unsuitable for feature transfer. Soft Temporal Alignment Distillation lets each student segment adaptively retrieve and aggregate teacher temporal evidence into its distillation target. Alignment denotes student-conditioned representation-space matching rather than chronological or monotonic registration. On the Multi-Modal Non-Intrusive 4D Human Dataset (MM-Fi), the method improves radar-only ac?curacy from 87.04% to 89.17%, achieving the highest mean accuracy among the evaluated established and recent distillation baselines. It also obtains higher mean accuracy and macro?averaged F1 score (macro-F1) than the radar student under most evaluated point- and frame-dropout settings. Adaptation to our independently collected millimeter-wave Office Human Ac?tivity Recognition Dataset (mmO-HAR) improves accuracy from 80.33% to 87.75%, supporting the transferability of the distilled radar representation. The teacher and alignment branch are removed after training, enabling privacy-preserving radar-only inference without additional sensing or computational branches.