tools/data/thumos14/README.md

Preparing THUMOS'14

Introduction

@misc{THUMOS14,
    author = {Jiang, Y.-G. and Liu, J. and Roshan Zamir, A. and Toderici, G. and Laptev,
    I. and Shah, M. and Sukthankar, R.},
    title = {{THUMOS} Challenge: Action Recognition with a Large
    Number of Classes},
    howpublished = "\url{http://crcv.ucf.edu/THUMOS14/}",
    Year = {2014}
}

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/thumos14/.

Step 1. Prepare Annotations

First of all, run the following script to prepare annotations.

cd $MMACTION2/tools/data/thumos14/
bash download_annotations.sh

Step 2. Prepare Videos

Then, you can run the following script to prepare videos.

cd $MMACTION2/tools/data/thumos14/
bash download_videos.sh

Step 3. 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.

You can run the following script to soft link SSD.

# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/thumos14_extracted/
ln -s /mnt/SSD/thumos14_extracted/ ../data/thumos14/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.

cd $MMACTION2/tools/data/thumos14/
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.

cd $MMACTION2/tools/data/thumos14/
bash extract_rgb_frames_opencv.sh

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

cd $MMACTION2/tools/data/thumos14/
bash extract_frames.sh tvl1

Step 4. Fetch File List

This part is optional if you do not use SSN model.

You can run the follow script to fetch pre-computed tag proposals.

cd $MMACTION2/tools/data/thumos14/
bash fetch_tag_proposals.sh

Step 5. Denormalize Proposal File

This part is optional if you do not use SSN model.

You can run the follow script to denormalize pre-computed tag proposals according to actual number of local rawframes.

cd $MMACTION2/tools/data/thumos14/
bash denormalize_proposal_file.sh

Step 6. Check Directory Structure

After the whole data process for THUMOS'14 preparation, you will get the rawframes (RGB + Flow), videos and annotation files for THUMOS'14.

In the context of the whole project (for THUMOS'14 only), the folder structure will look like:

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── thumos14
│   │   ├── proposals
│   │   |   ├── thumos14_tag_val_normalized_proposal_list.txt
│   │   |   ├── thumos14_tag_test_normalized_proposal_list.txt
│   │   ├── annotations_val
│   │   ├── annotations_test
│   │   ├── videos
│   │   │   ├── val
│   │   │   |   ├── video_validation_0000001.mp4
│   │   │   |   ├── ...
│   │   |   ├── test
│   │   │   |   ├── video_test_0000001.mp4
│   │   │   |   ├── ...
│   │   ├── rawframes
│   │   │   ├── val
│   │   │   |   ├── video_validation_0000001
|   │   │   |   │   ├── img_00001.jpg
|   │   │   |   │   ├── img_00002.jpg
|   │   │   |   │   ├── ...
|   │   │   |   │   ├── flow_x_00001.jpg
|   │   │   |   │   ├── flow_x_00002.jpg
|   │   │   |   │   ├── ...
|   │   │   |   │   ├── flow_y_00001.jpg
|   │   │   |   │   ├── flow_y_00002.jpg
|   │   │   |   │   ├── ...
│   │   │   |   ├── ...
│   │   |   ├── test
│   │   │   |   ├── video_test_0000001

For training and evaluating on THUMOS'14, please refer to Training and Test Tutorial.