Summary of Rt-pose: a 4d Radar Tensor-based 3d Human Pose Estimation and Localization Benchmark, by Yuan-hao Ho et al.
RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark
by Yuan-Hao Ho, Jen-Hao Cheng, Sheng Yao Kuan, Zhongyu Jiang, Wenhao Chai, Hsiang-Wei Huang, Chih-Lung Lin, Jenq-Neng Hwang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel radar-based approach for human pose estimation (HPE) is introduced, which addresses limitations of traditional RGB-image-based methods by providing through-wall recognition and privacy preservation. The Radar Tensor-based HPE dataset (RT-Pose) and an open-source benchmarking framework are presented. RT-Pose comprises 4D radar tensors, LiDAR point clouds, and RGB images, collected across six actions with varying complexity levels. An annotation process using RGB images and LiDAR point clouds accurately labels 3D human skeletons. A single-stage architecture, HRRadarPose, is proposed to extract high-resolution representations from 4D radar tensors for keypoint estimation, outperforming previous work on the RT-Pose benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radar-based human pose estimation can help keep people safe and private. This paper creates a special dataset with radar signals, LiDAR points, and regular images. They also make an open-source tool to test how well different approaches do. The dataset has over 72,000 frames of people doing six different actions, from simple to complex. It’s hard to get accurate human pose estimation in real-world situations, but this paper shows that radar signals can be a helpful way to do it. |
Keywords
» Artificial intelligence » Pose estimation
