News

What is EPIC-Kitchens?

The largest dataset in first-person (egocentric) vision; multi-faceted non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities in the kitchen over multiple days. Annotations are collected using a novel `live' audio commentary approach.

Characteristics

  • 32 kitchens - 4 cities
  • Head-mounted camera
  • 55 hours of recording - Full HD, 60fps
  • 11.5M frames
  • Multi-language narrations
  • 39,594 action segments
  • 454,255 object bounding boxes
  • 125 verb classes, 331 noun classes

Updates

Stay tuned with updates on epic-kitchens2018, as well as EPIC workshop series by joining the epic-community mailing list send an email to: sympa@sympa.bristol.ac.uk with the subject subscribe epic-community and a blank message body.

EPIC-Kitchens Stats

Some graphical representations of our dataset and annotations

Time Of Day

Activities

Wordle of annotations

Download

Dataset publicly available for research purposes

Sequences and Annotations

Sequences: Available at Data.Bris servers (1TB zipped)

To download parts of the dataset, we provide three scripts for downloading the

Publication(s)

Cite the following paper (available now on Arxiv and the CVF):

@INPROCEEDINGS{Damen2018EPICKITCHENS,
   title={Scaling Egocentric Vision: The EPIC-KITCHENS Dataset},
   author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria  and Fidler, Sanja and 
           Furnari, Antonino and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan 
           and Perrett, Toby and Price, Will and Wray, Michael},
   booktitle={European Conference on Computer Vision (ECCV)},
   year={2018}
} 

Copyright Creative Commons License

All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution-NonCommercial 4.0 International License. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.

Challenges

Challenge and Leaderboard Details

Action-Recognition Challenge

Given a trimmed action segment, the challenge is to classify the segment into its action class composed of the pair of verb and noun classes. To participate in this challenge, predictions for all segments in the seen (S1) and unseen (S2) test sets should be provided. For each test segment we require the confidence scores for each verb and noun class.


Get started with the starter pack

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

Action-Anticipation Challenge

Given an anticipation time, set to 1s before the action starts, the challenge is to classify the future action into its action class composed of the pair of verb and noun classes. To participate in this challenge, predictions for all segments in the seen (S1) and unseen (S2) test sets should be provided. For each test segment we require the confidence scores for each verb and noun class.


Get started with the starter pack

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

Object-Detection Challenge

This challenge focuses on object detection and localisation. Note that our annotations only capture the ‘active’ objects pre-, during- and post- interaction. To participate in the challenge bounding box predictions with confidence scores should be submitted for sampled frames from the seen (S1) and unseen (S2) test sets.

Submit your results on CodaLab website

Sample qualitative results from the challenge's baseline

The Team

We are a group of researchers working in computer vision from the University of Bristol, University of Toronto, and University of Catania.

Dima Damen

Principal Investigator
University of Bristol
United Kingom

Sanja Fidler

Co-Investigator
University of Toronto, NVIDIA AI Research
Canada

Giovanni Maria Farinella

Co-Investigator
University of Catania
Italy

Davide Moltisanti

(Apr 2017 - )
University of Bristol

Michael Wray

(Apr 2017 - )
University of Bristol

Hazel Doughty

(Apr 2017 - )
University of Bristol

Toby Perrett

(Apr 2017 - Apr 2018)
University of Bristol

Antonino Furnari

(Jul 2017 - )
University of Catania

Jonathan Munro

(Sep 2017 - )
University of Bristol

Evangelos Kazakos

(Sep 2017 - )
University of Bristol

Will Price

(Oct 2017 - )
University of Bristol

Sponsors

The dataset is sponsored by: