HomeIoT ModelsMediaPipe-Pose-Estimation

    MediaPipe-Pose-Estimation

    Detect and track human body poses in real-time images and video streams.

    The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of poses in an image.

    Qualcomm® QCS8550
    QCS8550 (Proxy)
    0.85ms
    Inference Time
    0-1MB
    Memory Usage
    107NPU
    Layers

    Technical Details

    Input resolution:256x256
    Number of parameters (MediaPipePoseDetector):815K
    Model size (MediaPipePoseDetector):3.14 MB
    Number of parameters (MediaPipePoseLandmarkDetector):3.37M
    Model size (MediaPipePoseLandmarkDetector):12.9 MB

    Applicable Scenarios

    • Accessibility
    • Augmented Reality
    • ARVR

    Licenses

    Source Model:APACHE-2.0
    Deployable Model:AI Model Hub License

    Tags

    • real-time
      A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.

    Supported IoT Devices

    • QCS8550 (Proxy)

    Supported IoT Chipsets

    • Qualcomm® QCS8550