Radar-based human pose estimation through multi-format feature fusion

Paper Title:

ProbRadarM3F: mmWave Radar based Human Skeletal Pose Estimation with Probability Map Guided Multi-Format Feature Fusion

Published on:

8 May 2024

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Bing Zhu,

Zixin He,

Weiyi Xiong,

Guanhua Ding,

Jianan Liu,

Tao Huang,

Wei Chen,

Wei Xiang

Bullets

Key Details

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Proposes ProbRadarM3F model for radar-based human pose estimation

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Extracts traditional heatmap features via FFT processing

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Generates spatial probability maps to guide positional encoding

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Fuses heatmap and positional features for performance gains

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Achieves new state-of-the-art on HuPR dataset for 14 keypoints

Explore the topics in this paper

heatmaps

multi-modal fusion

pose estimation

radar

spatial encoding

AI generated summary

Radar-based human pose estimation through multi-format feature fusion

This paper introduces ProbRadarM3F, a novel radar-based model for indoor human pose estimation. It fuses traditional heatmap features from radar signals with new positional encoding features guided by generated probability maps. This allows it to capture more of the latent spatial information in radar data. Experiments show ProbRadarM3F outperforms prior state-of-the-art methods on the HuPR dataset for 14 keypoint detection, demonstrating the value of multi-format radar feature fusion.