MoveNet Single Pose tracking on OpenVINO

Running Google MoveNet Single Pose models on OpenVINO.

A convolutional neural network model that runs on RGB images and predicts human joint locations of a single person. Two variant: Lightning and Thunder, the latter being slower but more accurate. MoveNet uses an smart cropping based on detections from the previous frame when the input is a sequence of frames. This allows the model to devote its attention and resources to the main subject, resulting in much better prediction quality without sacrificing the speed.

For Blazepose, a challenger, please visit : openvino_blazepose

Install

You need OpenVINO 2021.3 (does not work with 2021.2) and OpenCV installed on your computer and to clone/download this repository.

Run

Usage:

> python3 MovenetOpenvino.py -h                                               
usage: MovenetOpenvino.py [-h] [-i INPUT] [-p {16,32}]
                          [-m {lightning,thunder}] [--xml XML] [-d DEVICE]
                          [-s SCORE_THRESHOLD] [-o OUTPUT]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Path to video or image file to use as input
                        (default=0)
  -p {16,32}, --precision {16,32}
                        Precision (default=32
  -m {lightning,thunder}, --model {lightning,thunder}
                        Model to use (default=thunder
  --xml XML             Path to an .xml file for model
  -d DEVICE, --device DEVICE
                        Target device to run the model (default=CPU)
  -s SCORE_THRESHOLD, --score_threshold SCORE_THRESHOLD
                        Confidence score to determine whether a keypoint
                        prediction is reliable (default=0.200000)
  -o OUTPUT, --output OUTPUT
                        Path to output video file

Examples :

• To use default webcam camera as input, Thunder model on CPU :

  python3 MovenetOpenvino.py

• To use default webcam camera as input, Thunder model on MyriadX :

  python3 MovenetOpenvino.py -d MYRIAD

• To use a file (video or image) as input :

  python3 MovenetOpenvino.py -i filename

• To use Lightning instead of Thunder the version of the landmark model.

  python3 BlazeposeOpenvino.py -m lightning

Keypress	Function
space	Pause
c	Show/hide cropping region
f	Show/hide FPS

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Performance with OpenVINO

My FPS measurements on a 30 seconds video:

	CPU (i7700k)	MyriadX
MoveNet Thunder	62	11.2
MoveNet Lightning	114	20.1
BlazePose Full	114	12.0
BlazePose Lite	132	19.9

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The models

They were generated by PINTO and are also available there: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/115_MoveNet

Credits

• Google Next-Generation Pose Detection with MoveNet and TensorFlow.js
• Katsuya Hyodo a.k.a Pinto, the Wizard of Model Conversion !