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, which are general-purpose action recognition (400 actions) models for Kinetics-400 dataset.

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

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 steps.py for implementation details.

  • DataStep reads frames from the input video.
  • EncoderStep preprocesses a frame and feeds it to the encoder model to produce a frame embedding.
  • DecoderStep feeds embeddings produced by the EncoderStep to the decoder model and produces 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 action_recognition.py 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

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

usage: action_recognition.py [-h] -i INPUT -m_en M_ENCODER [-m_de M_DECODER]
[-l CPU_EXTENSION] [-d DEVICE] [--fps FPS]
[-lb LABELS] [--no_show] [-s LABEL_SMOOTHING]
[--seq DECODER_SEQ_SIZE]
[-u UTILIZATION_MONITORS]
Options:
-h, --help Show this help message and exit.
-i INPUT, --input INPUT
Required. Id of the video capturing device to open (to
open default camera just pass 0), path to a video or a
.txt file with a list of ids or video files (one
object per line)
-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. If not specified,
simple averaging of encoder's outputs over a time
window is applied
-l CPU_EXTENSION, --cpu_extension CPU_EXTENSION
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
--fps FPS Optional. FPS for renderer
-lb LABELS, --labels LABELS
Optional. Path to file with label names
--no_show Optional. Don't show output
-s LABEL_SMOOTHING, --smooth LABEL_SMOOTHING
Optional. Number of frames used for output label
smoothing
--seq DECODER_SEQ_SIZE
Optional. Length of sequence that decoder takes as
input
-u UTILIZATION_MONITORS, --utilization-monitors UTILIZATION_MONITORS
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.

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 https://download.01.org/opencv/.

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 action_recognition.py -m_en models/driver_action_recognition_tsd_0002_encoder.xml \
-m_de models/driver_action_recognition_tsd_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