High-Resolution Gait Micro-Doppler Synthesis from Videos Over Diverse Trajectories

Shubo Yang, Soheil Hor, Jaeho Choi, Amin Arbabian

Abstract: In recent years, there has been increasing interest in human body motion analysis, with applications in activity classification, human gait analysis, and human intent recognition. Among the various methods, millimeter-wave (mmWave)-based approaches have become popular due to their inherent contactless and privacy-preserving properties, as well as their resilience to lighting, weather conditions, and measurement distance. However, mmWave-based human motion analysis is still in its early stages, primarily due to the limited availability of large-scale datasets. This is particularly true for tasks that require high-resolution motion measurements. In this work, we introduce a novel method for synthesizing high-resolution mmWave datasets directly from videos. The proposed approach is well-suited for applications requiring very high-resolution Doppler signature simulations, such as analyzing subtle hand motions of walking pedestrians. We achieve this by employing an adversarial training strategy combined with custom task-specific loss functions that enhance the micro-motion signatures in the hands and legs of walking pedestrians. This work is the first to design and validate a high-resolution synthesized Doppler dataset of walking activities across multiple trajectories and subjects.

External IDs:dblp:conf/icassp/0002HCA25