mmPred: Radar-based Human Motion Prediction in the Dark
Junqiao Fan, Haocong Rao, Jiarui Zhang, Jianfei Yang, Lihua Xie

TL;DR
This paper introduces mmPred, a novel radar-based human motion prediction framework using diffusion models, which is robust to lighting and privacy concerns, and outperforms existing RGB-D methods.
Contribution
The work pioneers the use of radar for human motion prediction with a diffusion-based approach, addressing radar-specific noise and artifacts with dual-domain representations and a transformer backbone.
Findings
Achieves 8.6% improvement on mmBody dataset
Achieves 22% improvement on mm-Fi dataset
Outperforms existing methods significantly
Abstract
Existing Human Motion Prediction (HMP) methods based on RGB-D cameras are sensitive to lighting conditions and raise privacy concerns, limiting their real-world applications such as firefighting and healthcare. Motivated by the robustness and privacy-preserving nature of millimeter-wave (mmWave) radar, this work introduces radar as a novel sensing modality for HMP, for the first time. Nevertheless, radar signals often suffer from specular reflections and multipath effects, resulting in noisy and temporally inconsistent measurements, such as body-part miss-detection. To address these radar-specific artifacts, we propose mmPred, the first diffusion-based framework tailored for radar-based HMP. mmPred introduces a dual-domain historical motion representation to guide the generation process, combining a Time-domain Pose Refinement (TPR) branch for learning fine-grained details and a…
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Gait Recognition and Analysis · Non-Invasive Vital Sign Monitoring
