Point‐convolution‐based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple‐input multiple‐output radar
Author:
Affiliation:
1. Institute of Information and Communication Guilin University of Electronic Technology Guilin China
2. Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China
Publisher
Institution of Engineering and Technology (IET)
Subject
Computer Vision and Pattern Recognition,Signal Processing,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/bme2.12081
Reference45 articles.
1. Skeleton-Based Action Recognition With Shift Graph Convolutional Network
2. ImageNet classification with deep convolutional neural networks
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2. Human Pose Inference Using an Elevated mmWave FMCW Radar;IEEE Access;2024
3. Egocentric Human Pose Estimation using Head-mounted mmWave Radar;Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems;2023-11-12
4. A Robust Multi-Frame mmWave Radar Point Cloud-Based Human Skeleton Estimation Approach with Point Cloud Reliability Assessment;2023 IEEE SENSORS;2023-10-29
5. Human Parsing with Joint Learning for Dynamic mmWave Radar Point Cloud;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2022-03-27
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