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2-stream CNN architectures for action detection and recognition with attention filtering

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Two Stream Attention Convolutional Neural Networks

Contributions

We provide code for Two Stream Action Detection in Keras and each respective extension. Much of the credit of this repo goes to @joaogantunes and @ruipimentelfigueiredo.

Prerequisites

  • python >= 2.7
  • numpy (pip install numpy)
  • ffmpeg built with gstreamer (sudo apt install ffmpeg)
  • OpenCV 2.X or 3.X (build with opencv-contrib and ffmpeg and CUDA support)
  • Pillow (pip install Pillow)
  • CUDA + cuDNN (we used 9 and 7.1, respectively)
  • Tensorflow (for backend GPU) (pip install tensorflow-gpu)
  • Keras >= 2.1.6 (pip install keras)
  • Pandas (pip install pandas) and SciPy (pip install scipy) (because of dependencies)
  • Maplotlib + Seaborn (pip install seaborn) for plots
  • (Optional) To use Keras to draw models you need graphviz and pydot.

Data

  • AVA -- For the AVA dataset due to computational power constraints and for quickly testing the architecture, we made our own split of the dataset called mini-AVA, you can download it here

Models

You can get our Keras models here, includes pre-trained Kinetics, UCF101 models and our AVA models pre-trained on both.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

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  • Python 88.1%
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