Hand Tracking on Jetson Orin Nano

Issue Overview

Users are experiencing difficulties implementing hand tracking functionality using the Nvidia Jetson Orin Nano, specifically with the MediaPipe library. The main symptoms include failure to compile MediaPipe from source, inability to utilize GPU acceleration for TensorFlow Lite (TFLite), and low frame rates (around 5 FPS) that are insufficient for interactive applications. The problem arises during setup and attempts to run hand tracking models, particularly in Python environments. Users have reported varying levels of success with different JetPack versions (5.1.3 and 6.0.2) and configurations involving OpenCV and TensorFlow, but consistent performance issues remain. The impact on user experience is significant, as the inability to achieve functional hand tracking limits project capabilities and performance expectations.

Possible Causes

• Hardware Incompatibilities: The Jetson Orin Nano may not be fully compatible with certain libraries or models designed for other Jetson boards or architectures.

• Software Bugs or Conflicts: Issues may arise from bugs within MediaPipe or conflicts with other installed libraries (e.g., OpenCV, TensorFlow).

• Configuration Errors: Incorrect settings during installation or compilation processes may lead to failures in utilizing GPU resources or achieving expected performance.

• Driver Issues: Outdated or incompatible drivers for CUDA or TFLite could hinder the proper functioning of hardware acceleration.

• Environmental Factors: Power supply inconsistencies or thermal issues could affect performance stability.

• User Errors or Misconfigurations: Users may inadvertently misconfigure their development environments or fail to follow proper installation procedures.

Troubleshooting Steps, Solutions & Fixes

1. Diagnosing the Problem:

   • Verify the installed version of JetPack using the command:

     dpkg -l | grep nvidia-l4t-core
     

   • Check for any error messages during MediaPipe compilation and ensure all dependencies are met.

2. Gathering System Information:

   • Use the following command to check GPU utilization:

     nvidia-smi
     

3. Isolating the Issue:

   • Test with a minimal example of MediaPipe to determine if the issue is project-specific.
   • Attempt to run a simple TFLite model to see if GPU acceleration works independently of MediaPipe.

4. Potential Fixes:

   • Ensure that you are using compatible versions of CUDA, TensorFlow, and MediaPipe as per the official documentation.
   • If using JetPack 6.x, consider reverting to JetPack 5.x if stability issues persist.
   • Follow detailed instructions for building MediaPipe from source specifically tailored for Jetson boards, which can be found in community forums and documentation links shared by users.

5. Workarounds:

   • If GPU acceleration is not functioning, consider running models on CPU temporarily while troubleshooting GPU issues.
   • Explore alternative libraries for hand tracking that may be more compatible with Jetson Orin Nano.

6. Documentation & Updates:

   • Regularly check Nvidia’s developer forums and GitHub repositories for updates on MediaPipe compatibility and bug fixes.
   • Keep your system updated with the latest drivers and patches from Nvidia.

7. Best Practices:

   • Always back up your configurations before making significant changes.
   • Document any changes made during troubleshooting for future reference.

8. Unresolved Aspects:

   • Users have noted ongoing difficulties in achieving satisfactory performance levels with hand tracking models on the Jetson Orin Nano.
   • Further investigation into specific library compatibility and performance tuning may be necessary as more users report similar issues.

By following these structured troubleshooting steps and solutions, users can better navigate the challenges associated with implementing hand tracking on the Nvidia Jetson Orin Nano.