Action Recognition Python* Demo

This is the demo application for Action Recognition algorithm, which classifies actions that are being performed on input video. The following pre-trained models are delivered with the product:

How It Works

The demo pipeline consists of several frames, namely Data, Encoder, Decoder and Render. Every step implements PipelineStep interface by creating a class derived from PipelineStep base class. See for implementation details.

Pipeline steps are composed in AsyncPipeline. Every step can be run in separate thread by adding it to the pipeline with parallel=True option. When two consequent steps occur in separate threads, they communicate via message queue (for example, deliver step result or stop signal).

To ensure maximum performance, Inference Engine models are wrapped in AsyncWrapper that uses Inference Engine async API by scheduling infer requests in cyclical order (inference on every new input is started asynchronously, result of the longest working infer request is returned). You can change the value of num_requests in to find an optimal number of parallel working infer requests for your inference accelerators (Compute Sticks and GPUs benefit from higher number of infer requests).

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_channels argument 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:

usage: [-h] --encoder ENCODER --decoder DECODER [-v VIDEO]
[-d DEVICE] [--fps FPS] [-l LABELS]
-h, --help show this help message and exit
--encoder ENCODER Required. Path to encoder model
--decoder DECODER Required. Path to decoder model
-v VIDEO, --video VIDEO
Optional. Path to a video or file. 'cam' for capturing
-vl VIDEO_LIST, --video_list VIDEO_LIST
Optional. Path to a list with video files (text file,
one video per line)
Optional. For CPU custom layers, if any. Absolute path
to a shared library with the kernels implementation.
-pp PLUGIN_DIR, --plugin_dir PLUGIN_DIR
Optional. Path to a plugin folder
-d DEVICE, --device DEVICE
Optional. Specify a target device to infer on. CPU, GPU, FPGA, HDDL or MYRIAD is "
acceptable. The demo will look for a suitable plugin for the device specified.
Default value is CPU
--fps FPS Optional. FPS for renderer
-l LABELS, --labels LABELS
Optional. Path to file with label names

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

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to

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 run the demo for in-cabin driver monitoring scenario, please provide a path to the encoder and decoder models, an input video and a file with label names:

python3 --encoder models/driver_action_recognition_tsd_0002_encoder.xml \
--decoder models/driver_action_recognition_tsd_0002_decoder.xml \
--labels driver_actions.txt

Demo Output

The application uses OpenCV to display the real-time results and current inference performance (in FPS).

See Also