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 is a model for driver monitoring scenario. It recognizes actions like safe driving, talking to the phone and othersaction-recognition-0001-encoder
+ action-recognition-0001-decoder
, which is a general-purpose action recognition (400 actions) model for Kinetics-400 dataset.For more information about the pre-trained models, refer to https://github.com/opencv/open_model_zoo/blob/master/intel_models/index.md in the Open Model Zoo repository.
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 demo.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, 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 Reverse Input Channels 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 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:
The application uses OpenCV to display the real-time results and current inference performance (in FPS).