MoveNet Multipose Tracking on OpenVINO

Running Google MoveNet Multipose models on OpenVINO.

A convolutional neural network model that runs on RGB images and predicts human joint
of several persons (6 max).

WIP: currently only working on CPU (not on GPU nor MYRIAD)


Full video demo here.

For MoveNet Single Pose, please visit : openvino_movenet


You need OpenVINO (tested on 2021.4) and OpenCV installed on your computer and to clone/download this repository.



> python3 -h
usage: [-h] [-i INPUT] [--xml XML]
                            [-r {192x192,192x256,256x256,256x320,320x320,480x640,736x1280}]
                            [-t {iou,oks}] [-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
  --xml XML             Path to an .xml file for model
  -r {192x192,192x256,256x256,256x320,320x320,480x640,736x1280}, --res {192x192,192x256,256x256,256x320,320x320,480x640,736x1280}
  -t {iou,oks}, --tracking {iou,oks}
                        Enable tracking and specify method
                        Confidence score (default=0.200000)
  -o OUTPUT, --output OUTPUT
                        Path to output video file

Examples :

  • To use default webcam camera as input :


  • To specify the model input resolution :

    python3 -r 256x320

  • To enable tracking, based on Object Keypoint Similarity :

    python3 -t keypoint

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

    python3 -i filename

Keypress Function
Esc Exit
space Pause
b Show/hide bounding boxes
f Show/hide FPS

Input resolution

The model input resolution (set with the '-r' or '--res' option) has an impact on the inference speed (the higher the resolution, the slower the inference) and on the size of the people that can be detected (the higher the resoltion, the smaller the size).
The test below has been run on a CPU i7700k.

Resolution FPS Result
192x256 58.0 192x256
256x320 44.1 256x320
480x640 14.8 480x640
736x1280 4.5 736x1280


The Javascript MoveNet demo code from Google proposes as an option two methods of tracking. For this repository, I have adapted this tracking code in python. You can enable the tracking with the --tracking (or -t) argument of the demo followed by iou or oks which specifies how to calculate the similarity between detections from consecutive frames :

Tracking Result
IoU Tracking IoU Tracking
OKS Tracking OKS Tracking

In the example above, we can notice several track switching in the IoU output and a track replacement (2 by 6). OKS method is doing a better job, yet it is not perfect: there is a track switching when body 3 is passing in front of body 1.

The models

The MoveNet Multipose v1 source model comes from the Tensorfow Hub:

The model was converted by PINTO in OpenVINO IR format. Unfortunately, the OpenVINO IR MoveNet model input resolution cannot be changed dynamically, so an arbitrary list of models have been generated, each one with its dedicated input resolution. These models and others (other resolutions or precisions) are also available there: