This demo showcases the work of multi-person 2D pose estimation algorithm. The task is to predict a pose: body skeleton, which consists of keypoints and connections between them, for every person in an input video. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. Some of potential use cases of the algorithm are action recognition and behavior understanding. You can use the following pre-trained model with the demo:
human-pose-estimation-0001, which is a human pose estimation network, that produces two feature vectors. The algorithm uses these feature vectors to predict human poses.
For more information about the pre-trained model, refer to the https://github.com/opencv/open_model_zoo/blob/master/intel_models/index.md "Open Model Zoo" repository on GitHub*.
The input frame height is scaled to model height, frame width is scaled to preserve initial aspect ratio and padded to multiple of 8.
Other demo objectives are:
On the start-up, the application reads command line parameters and loads human pose estimation model. Upon getting a frame from the OpenCV VideoCapture, the application executes human pose estimation algorithm and displays the results.
NOTE: By default, Inference Engine samples and 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 sample or demo application or reconvert your model using the Model Optimizer tool with
--reverse_input_channelsargument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.
Running the application with the
-h option yields the following usage message:
Running the application with an empty list of options yields an error message.
NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.
For example, to do inference on a CPU, run the following command:
The demo uses OpenCV to display the resulting frame with estimated poses and text report of FPS - frames per second performance for the human pose estimation demo.