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.
Source Model
- Input shape: 1x3x224xx224
- Number of parameters: 49.89M
- Model size: 200.04M
- Output shape: 1x38x28x28, 1x19x28x28
The source model can be found here
When the user has fine-tuned the source model, the model conversion process must be performed again.
Users can refer to either of the following two methods to complete the model conversion:
Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.
Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.
The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.
For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:
Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation
Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation
Inference Example Code
The inference example code is implemented using the AidLite SDK.
Click Model & Code to download the model files and the inference code package. The file structure is as follows:
/model_farm_{model_name}_aidlite
|__ models # folder where model files are stored
|__ python # aidlite python model inference example
|__ cpp # aidlite cpp model inference example
|__ README.md