Egopet Dataset (ECCV 2022)

Amir Bar, Arya Bakhtiar, Danny Tran, Antonio Loquercio, Jathushan Rajasegaran, Yann LeCun, Amir Globerson, Trevor Darrell

This repository contains the implementation of the EgoPet research paper. For more information about this work, please visit the Project Page.

Abstract

Animals perceive the world to plan their actions and interact with other agents to accomplish complex tasks, demonstrating capabilities that are still unmatched by AI systems. To advance our understanding and reduce the gap between the capabilities of animals and AI systems, we introduce a dataset of pet egomotion imagery with diverse examples of simultaneous egomotion and multi-agent interaction. Current video datasets separately contain egomotion and interaction examples, but rarely both at the same time. In addition, EgoPet offers a radically distinct perspective from existing egocentric datasets of humans or vehicles. We define two in-domain benchmark tasks that capture animal behavior, and a third benchmark to assess the utility of EgoPet as a pretraining resource to robotic quadruped locomotion, showing that models trained from EgoPet outperform those trained from prior datasets. This work provides evidence that today's pets could be a valuable resource for training future AI systems and robotic assistants.

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Example Structure

Input

The Excel spreadsheet (egopet_dataset_spreadsheet.xlsx) containing video details such as URLs, availability, and segment information.

Dataset format

Processed videos are saved in the edited_downloaded_videos directory, with each video file prefixed with "edited_" in its filename. By the end, the folder structure should look like something below

.
├── EgoPet
│   ├── egopet_dataset_spreadsheet.xlsx
│   └── training_and_validation_test_set
│       ├── train
│       │   ├── cat
│       │   │   ├── edited_0a47448b479faca78b65f7d39d04b77a1ee4c55ef8154fb24061038c7b381761_segment_1.mp4
│       │   │   ├── edited_0a81227f2f3276024e3c9ff980e917ce4cb066608d6139aca18e0a7a761b9a5b_segment_1.mp4
│       │   │   └── ...
│       │   ├── dog
│       │   │   ├── edited_0e4b23e072ec674d5ece872d3558d52e5794fb27fa5e0780de58a10bad56ad5b_segment_078.mp4
│       │   │   ├── edited_0e4b23e072ec674d5ece872d3558d52e5794fb27fa5e0780de58a10bad56ad5b_segment_080.mp4
│       │   │   └── ...
│       │   └── ... (other animal folders)
│       └── validation
│           ├── cat
│           │   ├── edited_2cd4884c54a62fe9a41372412fc774f8d39afdd4f0c12c5473ad166d1b1c7b61_segment_7.mp4
│           │   ├── edited_2cd4884c54a62fe9a41372412fc774f8d39afdd4f0c12c5473ad166d1b1c7b61_segment_45.mp4
│           │   └── ...
│           ├── dog
│           │   ├── edited_3ef180ebd7674cf9e4f9dd736cc72d1cf20f978b4a70a0843e61319565774911_segment_5.mp4
│           │   ├── edited_3ef180ebd7674cf9e4f9dd736cc72d1cf20f978b4a70a0843e61319565774911_segment_6.mp4
│           │   └── ...
│           └── ... (other animal folders)

Reference

If you found this code useful, please cite the following paper:

@article{bar2024egopet,
  title={EgoPet: Egomotion and Interaction Data from an Animal's Perspective},
  author={Bar, Amir and Bakhtiar, Arya and Tran, Danny and Loquercio, Antonio and Rajasegaran, Jathushan and LeCun, Yann and Globerson, Amir and Darrell, Trevor},
  journal={arXiv preprint arXiv:2404.09991},
  year={2024}
}