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:

  • driver-action-recognition-adas-0002-encoder + driver-action-recognition-adas-0002-decoder, which are models for driver monitoring scenario. They recognize actions like safe driving, talking to the phone and others
  • action-recognition-0001-encoder + action-recognition-0001-decoder and i3d-rgb-tf, which are general-purpose action recognition (400 actions) models for Kinetics-400 dataset.

For more information about the pre-trained models, refer to the Intel model documentation and public model documentation.

How It Works

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

  • DataStep reads frames from the input video.
  • Model step depends on architecture type:
    • For encder-decoder models there are two steps:
      • EncoderStep preprocesses a frame and feeds it to the encoder model to produce a frame embedding. simple averaging of encoder's outputs over a time window is applied.
      • DecoderStep feeds embeddings produced by the EncoderStep to the decoder model and produces predictions. For models that use DummyDecoder simple averaging of encoder's outputs over a time window is applied.
    • For specific single models implemented corresponding <ModelNameStep> which does preprocess and produce predictions.
  • RenderStep renders prediction results.

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, 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 --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.


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

usage: [-h] -i INPUT [--loop] [-o OUTPUT]
[-limit OUTPUT_LIMIT] -at
{en-de,en-mean,i3d-rgb} -m_en M_ENCODER
[--no_show] [-s LABEL_SMOOTHING]
-h, --help Show this help message and exit.
-i INPUT, --input INPUT
Required. An input to process. The input must be a
single image, a folder of images, video file or camera
--loop Optional. Enable reading the input in a loop.
-o OUTPUT, --output OUTPUT
Optional. Name of output to save.
-limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT
Optional. Number of frames to store in output. If 0 is
set, all frames are stored.
-at {en-de,en-mean,i3d-rgb}, --architecture_type {en-de,en-mean,i3d-rgb}
Required. Specify architecture type.
-m_en M_ENCODER, --m_encoder M_ENCODER
Required. Path to encoder model.
-m_de M_DECODER, --m_decoder M_DECODER
Optional. Path to decoder model. Only for -at en-de.
Optional. Length of sequence that decoder takes as
Optional. For CPU custom layers, if any. Absolute path
to a shared library with the kernels implementation.
-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.
-lb LABELS, --labels LABELS
Optional. Path to file with label names.
--no_show Optional. Don't show output.
Optional. Number of frames used for output label

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. The list of models supported by the demo is in the models.lst file in the demo's directory.

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 \
-m_en <path_to_model>/driver-action-recognition-adas-0002_encoder.xml \
-m_de <path_to_model>/driver-action-recognition-adas-0002_decoder.xml \
-i <path_to_video>/inputVideo.mp4 \
-lb driver_actions.txt

Demo Output

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

See Also