[Submitted on 31 Dec 2022]
Abstract: Advances in computer vision and machine learning techniques have led to
significant development in 2D and 3D human pose estimation from RGB cameras,
LiDAR, and radars. However, human pose estimation from images is adversely
affected by occlusion and lighting, which are common in many scenarios of
interest. Radar and LiDAR technologies, on the other hand, need specialized
hardware that is expensive and power-intensive. Furthermore, placing these
sensors in non-public areas raises significant privacy concerns. To address
these limitations, recent research has explored the use of WiFi antennas (1D
sensors) for body segmentation and key-point body detection. This paper further
expands on the use of the WiFi signal in combination with deep learning
architectures, commonly used in computer vision, to estimate dense human pose
correspondence. We developed a deep neural network that maps the phase and
amplitude of WiFi signals to UV coordinates within 24 human regions. The
results of the study reveal that our model can estimate the dense pose of
multiple subjects, with comparable performance to image-based approaches, by
utilizing WiFi signals as the only input. This paves the way for low-cost,
broadly accessible, and privacy-preserving algorithms for human sensing.
Submission history
From: Jiaqi Geng [view email]
[v1]
Sat, 31 Dec 2022 16:48:43 UTC (16,746 KB)
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Rubi Geddes