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Preparing Moments in Time

Introduction

[DATASET]

@article{monfortmoments,
    title={Moments in Time Dataset: one million videos for event understanding},
    author={Monfort, Mathew and Andonian, Alex and Zhou, Bolei and Ramakrishnan, Kandan and Bargal, Sarah Adel and Yan, Tom and Brown, Lisa and Fan, Quanfu and Gutfruend, Dan and Vondrick, Carl and others},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2019},
    issn={0162-8828},
    pages={1--8},
    numpages={8},
    doi={10.1109/TPAMI.2019.2901464},
}

For basic dataset information, you can refer to the dataset website. Before we start, please make sure that the directory is located at $MMACTION2/tools/data/mit/.

Step 1. Prepare Annotations and Videos

First of all, you have to visit the official website, fill in an application form for downloading the dataset. Then you will get the download link. You can use bash preprocess_data.sh to prepare annotations and videos. However, the download command is missing in that script. Remember to download the dataset to the proper place follow the comment in this script.

For better decoding speed, you can resize the original videos into smaller sized, densely encoded version by:

python ../resize_videos.py ../../../data/mit/videos/ ../../../data/mit/videos_256p_dense_cache --dense --level 2

Step 2. Extract RGB and Flow

This part is optional if you only want to use the video loader.

Before extracting, please refer to install.md for installing denseflow.

If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. And you can run the following script to soft link the extracted frames.

# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/mit_extracted/
ln -s /mnt/SSD/mit_extracted/ ../../../data/mit/rawframes

If you only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract RGB-only frames using denseflow.

bash extract_rgb_frames.sh

If you didn't install denseflow, you can still extract RGB frames using OpenCV by the following script, but it will keep the original size of the images.

bash extract_rgb_frames_opencv.sh

If both are required, run the following script to extract frames.

bash extract_frames.sh

Step 4. Generate File List

you can run the follow script to generate file list in the format of rawframes and videos.

bash generate_{rawframes, videos}_filelist.sh

Step 5. Check Directory Structure

After the whole data process for Moments in Time preparation, you will get the rawframes (RGB + Flow), videos and annotation files for Moments in Time.

In the context of the whole project (for Moments in Time only), the folder structure will look like:

mmaction2
├── data
│   └── mit
│       ├── annotations
│       │   ├── license.txt
│       │   ├── moments_categories.txt
│       │   ├── README.txt
│       │   ├── trainingSet.csv
│       │   └── validationSet.csv
│       ├── mit_train_rawframe_anno.txt
│       ├── mit_train_video_anno.txt
│       ├── mit_val_rawframe_anno.txt
│       ├── mit_val_video_anno.txt
│       ├── rawframes
│       │   ├── training
│       │   │   ├── adult+female+singing
│       │   │   │   ├── 0P3XG_vf91c_35
│       │   │   │   │   ├── flow_x_00001.jpg
│       │   │   │   │   ├── flow_x_00002.jpg
│       │   │   │   │   ├── ...
│       │   │   │   │   ├── flow_y_00001.jpg
│       │   │   │   │   ├── flow_y_00002.jpg
│       │   │   │   │   ├── ...
│       │   │   │   │   ├── img_00001.jpg
│       │   │   │   │   └── img_00002.jpg
│       │   │   │   └── yt-zxQfALnTdfc_56
│       │   │   │   │   ├── ...
│       │   │   └── yawning
│       │   │       ├── _8zmP1e-EjU_2
│       │   │       │   ├── ...
│       │   └── validation
│       │   │       ├── ...
│       └── videos
│           ├── training
│           │   ├── adult+female+singing
│           │   │   ├── 0P3XG_vf91c_35.mp4
│           │   │   ├── ...
│           │   │   └── yt-zxQfALnTdfc_56.mp4
│           │   └── yawning
│           │       ├── ...
│           └── validation
│           │   ├── ...
└── mmaction
└── ...

For training and evaluating on Moments in Time, please refer to getting_started.md.