mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs
Arindam Sengupta, Feng Jin, Renyuan Zhang, Siyang Cao

TL;DR
This paper introduces mm-Pose, a real-time skeletal posture estimation system using mmWave radar and CNNs, capable of detecting over 15 joints, robust to lighting and weather, with applications in various fields.
Contribution
The paper presents the first method to detect more than 15 skeletal joints using mmWave radar signals and introduces a novel radar-to-image representation for improved accuracy.
Findings
Successfully detected and tracked human skeletal joints in real-time.
Validated the system with four primary human motions.
Achieved accurate 3D joint position predictions.
Abstract
In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals. The proposed method would find several applications in traffic monitoring systems, autonomous vehicles, patient monitoring systems and defense forces to detect and track human skeleton for effective and preventive decision making in real-time. The use of radar makes the system operationally robust to scene lighting and adverse weather conditions. The reflected radar point cloud in range, azimuth and elevation are first resolved and projected in Range-Azimuth and Range-Elevation planes. A novel low-size high-resolution radar-to-image representation is also presented, that overcomes the sparsity in traditional point cloud…
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