RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
By Tao Jiang, Peng Lu, Li Zhang, Ning Ma, Rui Han, Chengqi Lyu, Yining Li, Kai Chen. arXiv.org, 2023
424 citations recorded by the source corpus.
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency. In order to bridge this gap, we empirically explore key factors in pose estimation including paradigm, model architecture, training strategy, and deployment, and present a high-performance real-time multi-person pose estimation framework, RTMPose, based on MMPose. Our RTMPose-m achieves 75.8% AP on COCO with 90+ FPS on an Intel i7-11700 CPU and 430+ FPS on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves 67.0% AP on COCO-WholeBody with 130+ FPS. To further evaluate RTMPose's capability in critical real-time applications, we also report the performance after deploying on the mobile device. Our RTMPose-s achieves 72.2% AP on COCO with 70+ FPS on a Snapdragon 865 chip, outperforming existing open-source libraries. Code and models are released at https://github.com/open-mmlab/mmpose/tree/1.x/projects/rtmpose.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/7fc39b00981e017864ed01f9d5fdc27a1553e11a
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Citations in the corpus
- Effective Whole-body Pose Estimation with Two-stages Distillation (2023, methodology): “RTMPose [18] predicts pose keypoints with a SimCCbased [22] algorithm that treats keypoint localization as a classification task for horizontal and vertical coordinates.”