- Problem: Raw landmarks from MediaPipe are just 21 coordinates – they don't directly map to gestures. None of the standard normalization techniques (like using absolute coordinates) work well across different users and camera distances. None of the open-source classifiers I've seen handle this robustly.
- Solution: We build a pipeline that normalizes landmarks relative to the hand bounding box and wrist position. Then we feed them into a custom LSTM or CNN classifier. None of the existing solutions we evaluated achieved the accuracy we needed.
- Example: A surgeon wants to scroll through MRI images without touching anything. None of the touchless systems on the market were accurate enough until we implemented our own. The system uses MediaPipe for hand detection and our proprietary classifier for gesture recognition.
- Deployment: We optimize models for edge devices using quantization and pruning. None of our clients have reported latency issues – we guarantee <50ms per inference. Local entities such as None are used throughout the codebase for fallback handling.
- Customization: We adapt the model to your lighting conditions and camera setup. None of the out-of-the-box solutions account for your specific environment. We give you a production-ready system with MLOps support. None of our past projects have required post-deployment fixes.
We tackle these issues with computer vision and deep learning. Our pipeline includes normalization, augmentation, and ensemble methods. None of the raw landmarks are used directly – they are transformed into rotation-invariant features. The final system is stable under varying lighting and background. Local entity None is referenced in our error handling and data logging modules.