Traditional methods for human localization and pose estimation (HPE), which mainly rely on RGB images as an input modality, confront substantial limitations in real-world applications due to privacy concerns. In contrast, radar-based HPE methods emerge as a promising alternative, characterized by distinctive attributes such as through-wall recognition and privacy-preserving, rendering the method more conducive to practical deployments. This paper presents a Radar Tensor-based human pose (RT-Pose) dataset and an open-source benchmarking framework. RT-Pose dataset comprises 4D radar tensors, LiDAR point clouds, and RGB images, and is collected for a total of 72k frames across 240 sequences with six different complexity level actions. The 4D radar tensor provides raw spatio-temporal information, differentiating it from other radar point cloud-based datasets. We develop an annotation process, which uses RGB images and LiDAR point clouds to accurately label 3D human skeletons. In addition, we propose HRRadarPose, the first single-stage architecture that extracts the high-resolution representation of 4D radar tensors in 3D space to aid human keypoint estimation. HRRadarPose outperforms previous radar-based HPE work on the RT-Pose benchmark. The overall HRRadarPose performance on the RT-Pose dataset, as reflected in a mean per joint position error (MPJPE) of 9.91cm, indicates the persistent challenges in achieving accurate HPE in complex real-world scenarios.
RT-Pose is available on Hugging Face.
Please check data processing
We recommend using conda to manage the Python environment:
conda create -n rt_pose python=3.9 -y
conda activate rt_pose
pip install -r requirements-torch.txt --index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
cd det3d/ops/dcn && python setup.py build_ext --inplace
cd ../../..To train the baseline method:
python ./tools/train.py configs/<config file>For more details about the command options,
python ./tools/train.py -hTo evaluate the baseline method:
python ./tools/test.py <config file> --checkpoint <model checkpoint> --testsetFor more details about the command options,
python ./tools/test.py -hRT-Pose is released under CC BY-NC-SA.
Yuan-Hao Ho (n28081527@gs.ncku.edu.tw), Jen-Hao(Andy) Cheng(andyhci@uw.edu)
Our project could not have been accomplished without leveraging several exceptional open-source codebases.