Multi-Channel Human Pose Estimation C++ Demo

This demo provides an inference pipeline for Multi-Channel Human Pose Estimation. The demo uses Human Pose Estimation network.

Other demo objectives are:

  • Up to 16 cameras as inputs, via OpenCV*

  • Visualization of detected faces from all channels on a single screen

How It Works

On startup, the application reads command line parameters and loads the specified networks. The Human Pose Estimation network is required.


By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with the --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Preparing to Run

For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in <omz_dir>/demos/multi_channel_human_pose_estimation_demo/cpp/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (*.xml + *.bin).

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/ --list models.lst

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/ --list models.lst

Supported Models

  • human-pose-estimation-0001


Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the application with the -h option yields the following usage message:

multi_channel_human_pose_estimation_demo [OPTION]

    -h                           Print a usage message
    -i                           Required. A comma separated list of inputs to process. Each input must be a single image, a folder of images or anything that cv::VideoCapture can process.
    -loop                        Optional. Enable reading the inputs in a loop.
    -duplicate_num               Optional. Multiply the inputs by the given factor. For example, if only one input is provided, but -duplicate_num is set to 2, the demo will split real input across channels, by interleaving frames between channels.
    -m "<path>"                  Required. Path to an .xml file with a trained model.
      -l "<absolute_path>"       Required for CPU custom layers. Absolute path to a shared library with the kernel implementations
      -c "<absolute_path>"       Required for GPU custom kernels. Absolute path to an .xml file with the kernel descriptions
    -d "<device>"                Optional. Specify the target device for a network (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The demo looks for a suitable plugin for a specified device.
    -bs                          Optional. Batch size for processing (the number of frames processed per infer request)
    -nireq                       Optional. Number of infer requests
    -n_iqs                       Optional. Frame queue size for input channels
    -fps_sp                      Optional. FPS measurement sampling period between timepoints in msec
    -n_sp                        Optional. Number of sampling periods
    -pc                          Optional. Enable per-layer performance report
    -no_show                     Optional. Don't show output.
    -show_stats                  Optional. Enable statistics report
    -real_input_fps              Optional. Disable input frames caching, for maximum throughput pipeline
    -u                           Optional. List of monitors to show initially.

Running the application with an empty list of options yields the usage message given above and an error message.

For example, to run the demo with the pre-trained Human Pose Estimation model on CPU with one camera, use the following command:

./multi_channel_human_pose_estimation_demo \
  -d CPU \
  -i 0 \
  -m <path_to_model>/human-pose-estimation-0001.xml

To run the demo using two recorded video files, use the following command:

./multi_channel_human_pose_estimation_demo \
  -d CPU \
  -i <path_to_file>/file1,<path_to_file>/file2 \
  -m <path_to_model>/human-pose-estimation-0001.xml

Video files will be processed simultaneously.

Input Video Sources

General parameter for input source is -i. You can run the demo on web cameras and video files simultaneously by specifying: -i <webcam_id0>,<webcam_id1>,<video_file1>,<video_file2> with paths to webcams and video files separated by a comma. To run the demo with a single input source (a web camera or a video file), but several channels, specify an additional parameter, duplicate_num, for example: -duplicate_num 4. You will see four channels. With several input sources, the -duplicate_num parameter will duplicate each of them.

Below are some examples of demo input specification:

-i <file1>,<file2>

To see all available web cameras, run the ls /dev/video* command. You will get output similar to the following:

user@user-PC:~ $ ls /dev/video*
/dev/video0 /dev/video1 /dev/video2

You can use -i option to connect all the three web cameras:

-i 0,1,2

To connect to IP cameras, use RTSP URIs:

-i rtsp://camera_address_1/,rtsp://camera_address_2/

Demo Output

The demo uses OpenCV to display the resulting frames with detections rendered as bounding boxes. On the top of the screen, the demo reports throughput in frames per second. You can also enable more detailed statistics in the output using the -show_stats option while running the demos.