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--- |
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license: other |
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license_name: openpose-license |
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license_link: https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE |
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pipeline_tag: keypoint-detection |
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tags: |
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- AIoT |
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- QNN |
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--- |
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## OpenPose: Pose Estimation |
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OpenPose, an open-source real-time multi-person pose estimation framework developed by Carnegie Mellon University, detects human body, hand, and facial key points. Using CNN and Part Affinity Fields (PAFs), it locates and associates joints in multi-person scenarios, widely applied in action recognition, motion analysis, human-computer interaction, and AR. Its strength lies in high-accuracy multi-person tracking and cross-platform compatibility (CPU/GPU), though resource-intensive for edge deployment. Challenges include occlusion handling, model optimization, and robustness in low-light conditions. |
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## Source Model |
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- Input shape: 1x3x224xx224 |
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- Number of parameters: 49.89M |
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- Model size: 200.04M |
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- Output shape: 1x38x28x28, 1x19x28x28 |
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The source model can be found [here](https://github.com/CMU-Perceptual-Computing-Lab/openpose) |
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## Performance Reference |
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Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) |
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## Inference & Model Conversion |
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Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models) |
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## License |
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- Source Model: [OPENPOSE_LICENSE](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE) |
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- Deployable Model: [OPENPOSE_LICENSE](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE) |
