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 (this is the lrcn network in the code).
4. Extract features from each frame with a ConvNet and pass the sequence to an MLP
5. Use a 3D convolutional network (has two versions of 3d conv to choose from)

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

You must also have ffmpeg installed in order to extract the video files. If ffmpeg isn't in your system path (ie. which ffmpeg doesn't return its path, or you're on an OS other than *nix), you'll need to update the path to ffmpeg in data/2_extract_files.py.

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

Notes: only select first 30 classes:

1. Train: 2282 avi, 476857 jpg
2. Test: 1151 avi, 186640 jpg

Before you can run the lstm and mlp, you need to extract features from the images with the CNN. This is done by running extract_features.py. If you want to limit to just the first N classes, you can set that option in the file.

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.

Training logs are saved to CSV and also to TensorBoard files. To see progress while training, run tensorboard --logdir=data/logs from the project root folder.

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.

This project thanks for the open project at github https://github.com/harvitronix/five-video-classification-methods