MediaPipe-Pose-Estimation
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Input Resolution | 256x256 |
| Detector Parameters | 815K |
| Landmark Detector Parameters | 3.37M |
| Paper | BlazePose: On-device Real-time Body Pose tracking |
What is MediaPipe-Pose-Estimation?
MediaPipe-Pose-Estimation is a sophisticated machine learning pipeline designed specifically for mobile devices that enables real-time human pose detection and tracking. It employs a dual-model architecture: a pose detector and a landmark detector, optimized for deployment on Qualcomm devices with NPU acceleration.
Implementation Details
The model consists of two main components: MediaPipePoseDetector (3.14MB) and MediaPipePoseLandmarkDetector (12.9MB). Both models are optimized for FP16 precision and can run on NPU hardware. The implementation supports multiple formats including TFLITE and ONNX, with impressive inference times as low as 0.56ms on modern devices like the Samsung Galaxy S24.
- Efficient dual-model architecture for accurate pose detection
- FP16 precision optimization for mobile deployment
- Support for multiple runtime formats (TFLITE, ONNX)
- NPU acceleration capability
Core Capabilities
- Real-time pose detection and tracking
- Body landmark detection with high accuracy
- Optimized performance on mobile devices
- Cross-platform compatibility
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient mobile-first design, utilizing a two-stage architecture that balances accuracy with performance. Its optimization for Qualcomm NPUs enables exceptional real-time performance with inference times under 1ms on modern devices.
Q: What are the recommended use cases?
The model is ideal for mobile applications requiring real-time pose estimation, including fitness apps, motion tracking systems, augmented reality applications, and interactive gaming experiences that need efficient pose detection capabilities.
