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Egocentric Audio-Visual Object Localization

This is the PyTorch implementation of the paper "<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Egocentric_Audio-Visual_Object_Localization_CVPR_2023_paper.pdf">Egocentric Audio-Visual Object Localization</a>."

Overview

<p align="center"> <img align="center" src="./fig/motivation.png" width=70%> </p>

We explore the task of egocentric audio-visual object localization, which aims to localize objects that emit sounds in the first-person recordings. In this work, we propose a new framework to address the uniqueness of egocentric videos by answering the following two questions: (1) how to associate visual content with audio representations while out-of-view sounds may exist; (2) how to persistently associate audio features with visual content that are captured under different viewpoints.

Epic Sounding Object dataset

Note, some videos are further filtered out and some bounding boxes are updated recently.

Prepare Dataset

  1. Download videos.

    a. Download Epic-Kitchens dataset from: https://epic-kitchens.github.io/2023 (The website provides scirt to download videos).

  2. Preprocess videos.

    a. Trim the video using Epic-Kitchens' original annotations, for example, the test video timestamps can be found at https://github.com/epic-kitchens/epic-kitchens-100-annotations/blob/master/EPIC_100_test_timestamps.csv.

    b. Extract waveforms at 11000Hz for all the videos.

  3. Data splits. Please follow the same train/test splits at https://github.com/epic-kitchens/epic-kitchens-100-annotations.

  4. Filter out silent clips. As the action recognition splits are developed based on action, not audio, there could be video clips that are silent or do not include meaningful sounds. We try to filter out some silent video clips to obtain a better training set, please refer to ./code/script/filter_silent_clips.py. (Optional, you can use the newly released <a href="https://epic-kitchens.github.io/epic-sounds/">EPIC-SOUND dataset</a> to obtain an audio-based training split)

Annotation Format

The annotations can be found at ./data/soundingobject.json.

Requirements

pip install -r requirements.txt

Training

  1. Process videos and prepare the data. a. Trim the video following https://github.com/epic-kitchens/epic-kitchens-100-annotations/blob/master/EPIC_100_train.csv and get the frames within [start_frame, stop_frame]. Store the data with the following directory structure

    folder_name (e.g., 'P01_01-00:00:00.14-00:00:03.37-8-202')
├── audio
|   ├── P01_01-00:00:00.14-00:00:03.37-8-202.wav
|
└── rgb_frames
|   ├── frame_0000000008.jpg
│   ├── frame_0000000009.jpg
│   ├── ...
│   ├── frame_0000000202.jpg
└──

    b. Create the index files train.csv. For each row, it stores the information: participant_id,video_id,start_timestamp,stop_timestamp,start_frame,stop_frame,narration,folder_dir. Note that you can change the format and revise the dataloader accordingly. An example is given as follows:

    participant_id, video_id, start_timestamp, stop_timestamp, start_frame, stop_frame, narration, folder_dir
P01, P01_01, 00:00:00.14, 00:00:03.37, 8, 202, open door, /YOUR_DIR/P01_01-00:00:00.14-00:00:03.37-8-202-open_door

  2. Train the localization model

bash ./scripts/train_localization.sh
  1. During training, checkpoints are saved to data/ckpt/MODEL_ID.

Citation

If you find our work useful for your research, please consider citing our paper. :smile:

@inproceedings{huang2023egocentric,
  title={Egocentric Audio-Visual Object Localization},
  author={Huang, Chao and Tian, Yapeng and Kumar, Anurag and Xu, Chenliang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={22910--22921},
  year={2023}
}

Acknowledgement

We borrowed a lot of code from CCoL and CoSep. We thank the authors for sharing their code. If you use our codes, please also consider cite their nice works.