# Five video classification methods

The five video classification methods:

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

## Requirements

This code requires you have Keras 2 and TensorFlow 1 or greater installed. Please see the `requirements.txt` file. To ensure you're up to date, run:

`pip install -r requirements.txt`

## 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 (still in the data folder) 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. You need to run these in order. Example:

`python 1_move_files.py`

`python 2_extract_files.py`

## 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.