README.md

Five video classification methods

The five video classification methods:

1. Classify one frame at a time with a ConvNet
2. Extract features from each frame with a ConvNet, passing the sequence to an RNN, in a separate network
3. Use a time-dstirbuted ConvNet, passing the features to an RNN, much like #2 but all in one network
4. Extract features from each frame with a ConvNet and pass the sequence to an MLP
5. Use a 3D convolutional network

See the accompanying blog post for full details: https://medium.com/@harvitronix/five-video-classification-methods-implemented-in-keras-and-tensorflow-99cad29cc0b5

Getting the data

First, download the dataset from UCF into the data folder:

cd data && wget http://crcv.ucf.edu/data/UCF101/UCF101.rar

Then extract it with unrar e UCF101.rar.

Next, create folders with mkdir train && mkdir test && mkdir sequences && mkdir checkpoints.

Now you can run the scripts in the data folder to move the videos to the appropriate place, extract their frames and make the CSV file the rest of the code references.

Extracting features

Before you can run Methods #4 and #5, you need to extract features from the images with the CNN. This is done by running extract_features.py. On my Dell with a GeFore 960m GPU, this takes about 8 hours. If you want to limit to just the first N classes, you can set that option in the file.

Running models

The CNN-only method (method #1 in the blog post) is run from train_cnn.py.

The rest of the models are run from train.py. There are configuration options you can set in that file to choose which model you want to run.

The models are all defined in models.py. Reference that file to see which models you are able to run in train.py.

UCF101 Citation

Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild., CRCV-TR-12-01, November, 2012.