In the realm of computer vision, the ability to detect and track human body poses in real-time is a game-changer for various applications, especially on mobile devices. With MediaPipe-Pose-Estimation, we unlock the potential for seamless integration of pose detection in images and video streams, all thanks to powerful machine learning models.

Introduction to MediaPipe Pose Landmark Detector

The MediaPipe Pose Landmark Detector is an advanced machine learning pipeline designed to predict bounding boxes and pose skeletons in images. With this model, developers can harness the capabilities of pose estimation, facilitating projects such as fitness tracking, augmented reality, and even interactive gaming.

This model is an implementation of MediaPipe-Pose-Estimation available on its GitHub repository.

Model Details

Let’s outline the specifications of the model:

• Model Type: Pose estimation
• Input resolution: 256×256
• Number of parameters (MediaPipePoseDetector): 815K
• Model size (MediaPipePoseDetector): 3.14 MB
• Number of parameters (MediaPipePoseLandmarkDetector): 3.37M
• Model size (MediaPipePoseLandmarkDetector): 12.9 MB

Device Compatibility

Here’s how the model performs across various devices:

Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	TFLite	0.837 ms	0 – 2 MB	FP16	NPU	MediaPipePoseDetector.tflite
Samsung Galaxy S23 Ultra (Android 13)	Snapdragon® 8 Gen 2	TFLite	1.011 ms	0 – 2 MB	FP16	NPU	MediaPipePoseLandmarkDetector.tflite

Installation Guide

To install this model and get started, simply use pip:

pip install qai-hub-models

Configure Qualcomm® AI Hub

To run this model on a cloud-hosted device, you’ll need to configure your Qualcomm® AI Hub:

1. Sign in to the Qualcomm® AI Hub with your Qualcomm® ID.
2. Navigate to `Account -> Settings -> API Token`.
3. Configure your client with the API token:

qai-hub configure --api_token API_TOKEN

For further information, visit the documentation.

Running a Demo

This package includes a straightforward demo to showcase the model:

python -m qai_hub_models.models.mediapipe_pose.demo

If you’re working in a Jupyter Notebook or Google Colab, use the following command:

%run -m qai_hub_models.models.mediapipe_pose.demo

Cloud-hosted Device Execution

To run the model on a cloud-hosted Qualcomm® device, utilize the following script:

python -m qai_hub_models.models.mediapipe_pose.export

Understanding the Model Compilation and Profiling

Imagine you have a chef (your model) who prepares a dish (the output) at a restaurant (the device). To ensure a great dining experience (accurate predictions), we need to take three crucial steps:

Step 1: Compile Model

First, the chef needs to gather the ingredients and prepare them according to the recipe. This is akin to tracing the model in memory and compiling it for on-device deployment using:

torch.jit.trace

Step 2: Performance Profiling

Once the dish is prepared, the next step is to taste-test the meal. This profiling ensures the dish is up to par with restaurant standards, akin to executing performance checks on the device.

hub.submit_profile_job

Step 3: Accuracy Verification

Finally, you want to ensure that the meal served matches the menu description. This is like verifying model accuracy through on-device inference jobs:

hub.submit_inference_job

Deployment to Android

The compiled models can be deployed using several runtimes:

• TensorFlow Lite (.tflite export): Check out this guide.
• QNN (.so export): Instructions can be found in this sample app.

Performance Metrics

For detailed performance metrics across various devices, you can explore the MediaPipe-Pose-Estimation performance page.

Troubleshooting and Support

If you encounter issues during installation or while running the demo, consider the following tips:

• Ensure you have installed all the dependencies listed in the documentation.
• Check if your Qualcomm® ID has the appropriate access permissions for the AI Hub.
• Make sure your device is supported and meets performance standards.
• For troubleshooting, refer to the official Qualcomm® AI Hub documentation.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.