Preparing UCF101-24

Introduction

@article{Soomro2012UCF101AD,
  title={UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild},
  author={K. Soomro and A. Zamir and M. Shah},
  journal={ArXiv},
  year={2012},
  volume={abs/1212.0402}
}

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/ucf101_24/.

Download and Extract

You can download the RGB frames, optical flow and ground truth annotations from google drive. The data are provided from MOC, which is adapted from act-detector and corrected-UCF101-Annots.

Note

The annotation of this UCF101-24 is from here, which is more correct.

After downloading the UCF101_v2.tar.gz file and put it in $MMACTION2/tools/data/ucf101_24/, you can run the following command to uncompress.

tar -zxvf UCF101_v2.tar.gz

Check Directory Structure

After uncompressing, you will get the rgb-images directory, brox-images directory and UCF101v2-GT.pkl for UCF101-24.

In the context of the whole project (for UCF101-24 only), the folder structure will look like:

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── ucf101_24
│   |   ├── brox-images
│   |   |   ├── Basketball
│   |   |   |   ├── v_Basketball_g01_c01
│   |   |   |   |   ├── 00001.jpg
│   |   |   |   |   ├── 00002.jpg
│   |   |   |   |   ├── ...
│   |   |   |   |   ├── 00140.jpg
│   |   |   |   |   ├── 00141.jpg
│   |   |   ├── ...
│   |   |   ├── WalkingWithDog
│   |   |   |   ├── v_WalkingWithDog_g01_c01
│   |   |   |   ├── ...
│   |   |   |   ├── v_WalkingWithDog_g25_c04
│   |   ├── rgb-images
│   |   |   ├── Basketball
│   |   |   |   ├── v_Basketball_g01_c01
│   |   |   |   |   ├── 00001.jpg
│   |   |   |   |   ├── 00002.jpg
│   |   |   |   |   ├── ...
│   |   |   |   |   ├── 00140.jpg
│   |   |   |   |   ├── 00141.jpg
│   |   |   ├── ...
│   |   |   ├── WalkingWithDog
│   |   |   |   ├── v_WalkingWithDog_g01_c01
│   |   |   |   ├── ...
│   |   |   |   ├── v_WalkingWithDog_g25_c04
│   |   ├── UCF101v2-GT.pkl

Note

The UCF101v2-GT.pkl exists as a cache, it contains 6 items as follows:

1. labels (list): List of the 24 labels.

2. gttubes (dict): Dictionary that contains the ground truth tubes for each video. A gttube is dictionary that associates with each index of label and a list of tubes. A tube is a numpy array with nframes rows and 5 columns, each col is in format like <frame index> <x1> <y1> <x2> <y2>.

3. nframes (dict): Dictionary that contains the number of frames for each video, like 'HorseRiding/v_HorseRiding_g05_c02': 151.

4. train_videos (list): A list with nsplits=1 elements, each one containing the list of training videos.

5. test_videos (list): A list with nsplits=1 elements, each one containing the list of testing videos.

6. resolution (dict): Dictionary that outputs a tuple (h,w) of the resolution for each video, like 'FloorGymnastics/v_FloorGymnastics_g09_c03': (240, 320).