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Preparing AVA

Introduction

@inproceedings{gu2018ava,
  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},
  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6047--6056},
  year={2018}
}

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

Step 1. Prepare Annotations

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

bash download_annotations.sh

This command will download ava_v2.1.zip for AVA v2.1 annotation. If you need the AVA v2.2 annotation, you can try the following script.

VERSION=2.2 bash download_annotations.sh

Step 2. Prepare Videos

Then, use the following script to prepare videos. The codes are adapted from the official crawler. Note that this might take a long time.

bash download_videos.sh

Or you can use the following command to downloading AVA videos in parallel using a python script.

bash download_videos_parallel.sh

Note that if you happen to have sudoer or have GNU parallel on your machine, you can speed up the procedure by downloading in parallel.

# sudo apt-get install parallel
bash download_videos_gnu_parallel.sh

Step 3. Cut Videos

Cut each video from its 15th to 30th minute and make them at 30 fps.

bash cut_videos.sh

Step 4. Extract RGB and Flow

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/ava_extracted/
ln -s /mnt/SSD/ava_extracted/ ../data/ava/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 ffmpeg by the following script.

bash extract_rgb_frames_ffmpeg.sh

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

bash extract_frames.sh

Step 5. Fetch Proposal Files

The scripts are adapted from FAIR's Long-Term Feature Banks.

Run the following scripts to fetch the pre-computed proposal list.

bash fetch_ava_proposals.sh

Step 6. Folder Structure

After the whole data pipeline for AVA preparation. you can get the rawframes (RGB + Flow), videos and annotation files for AVA.

In the context of the whole project (for AVA only), the minimal folder structure will look like: (minimal means that some data are not necessary: for example, you may want to evaluate AVA using the original video format.)

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── ava
│   │   ├── annotations
│   │   |   ├── ava_dense_proposals_train.FAIR.recall_93.9.pkl
│   │   |   ├── ava_dense_proposals_val.FAIR.recall_93.9.pkl
│   │   |   ├── ava_dense_proposals_test.FAIR.recall_93.9.pkl
│   │   |   ├── ava_train_v2.1.csv
│   │   |   ├── ava_val_v2.1.csv
│   │   |   ├── ava_train_excluded_timestamps_v2.1.csv
│   │   |   ├── ava_val_excluded_timestamps_v2.1.csv
│   │   |   ├── ava_action_list_v2.1_for_activitynet_2018.pbtxt
│   │   ├── videos
│   │   │   ├── 053oq2xB3oU.mkv
│   │   │   ├── 0f39OWEqJ24.mp4
│   │   │   ├── ...
│   │   ├── videos_15min
│   │   │   ├── 053oq2xB3oU.mkv
│   │   │   ├── 0f39OWEqJ24.mp4
│   │   │   ├── ...
│   │   ├── rawframes
│   │   │   ├── 053oq2xB3oU
|   │   │   │   ├── img_00001.jpg
|   │   │   │   ├── img_00002.jpg
|   │   │   │   ├── ...

For training and evaluating on AVA, please refer to getting_started.

Reference

  1. O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014