AutoHighlight is an automatic video summarizer utilizing action recognition via 3D convolutional neural networks.

• AutoHighlight utilizes Inflated 3D Convolution architecture from DeepMind to classify video snippets (architecture obtained from this Keras implementation).
• The example weight included in the repo is trained on association football (soccer) matches using the SoccerNet dataset (GitHub repo here).

• Train your own AI video summarizer using your own dataset!
• Includes a pre-trained network that you can use for summarizing soccer matches!

AutoHighlight was developed in Python 3.6.5 on AWS Deep Learning AMI (Ubuntu) Version 23.0. It uses the following open source packages:

• Keras - 2.2.4
• Tensorflow - 1.13.1
• OpenCV - 3.4.2
• Numpy - 1.15.4
• MoviePy - 0.2.3.5
• imageio - 2.5.0
• Scikit-learn (for evaluation only) -

AutoHighlight is open source with a public repository on GitHub.

AutoHighlight requires Python 3 to run.

Install the dependencies and clone AutoHighlight from GitHub repo.

$ git clone https://github.com/dshin13/autohighlight.git

To run an inference on a video file, navigate to AutoHighlight directory and use the following command:

$ python autohighlight.py <videopath> <output>.mp4

To create videos from pre-existing annotation files, use the following command:

$ python autohighlight.py <videopath> <output>.mp4 -a <annotation path>

If you have video files to annotate, use the following command:

$ python videoscan.py <parent directory>

This will create filename_pred.npy for every video file (.mkv) in the same folder as the video.

To create overlay video clips showing activations from specific I3D blocks, use the following command:

$ python activation_visualizer.py <videopath> -b <block index to probe (0-8)> 

This will create a file named overlay.mp4 in your current working directory.

To generate class-labeled video clips, use the following command from the home directory:

$ python utils/clip_parser.py <source directory> <target directory>

Please refer to clip_parser.py docstring to define an appropriate filter function first.

Afterwards, generate train/val/test split using the following command:

$ python train_test_split.py <source directory> <target directory>

To define a custom RGB-stream I3D classifier for N classes, use the following command:

$ python models/build_RGB_model.py <num_frames> <num_width> <num_height> N

The model can be trained by using the following command:

$ python train.py <training set directory> <validation set directory>

Model training parameters and optimizer definitions can be modified as necessary inside train.py.

To use your own model to run an inference on a video file, use the following command:

$ python autohighlight.py -s <videopath> -o <output> -m <your model>

• Write tests

Code and contents of this repository are released under MIT License.

Kinetics-pretrained weights were released by DeepMind under Apache 2.0 License.