🍴 Annotations for the EPIC Kitchens Dataset.
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README.md

EPIC Kitchens Dataset

CircleCI GitHub release arXiv-1804.02748 Data

EPIC-Kitchens is 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.

Authors

Dima Damen (1) Hazel Doughty (1) Giovanni Maria Farinella (3) Sanja Fidler (2) Antonino Furnari (3) Evangelos Kazakos (1) Davide Moltisanti (1) Jonathan Munro (1) Toby Perrett (1) Will Price (1) Michael Wray (1)

  • (1 University of Bristol)
  • (2 University of Toronto)
  • (3 University of Catania)

Contact: uob-epic-kitchens2018@bristol.ac.uk

Citing

When using the dataset, kindly reference:

@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}
} 

(Check publication here)

Dataset Details

Ground Truth

We provide ground truth for action segments and object bounding boxes.

  • Objects: Full bounding boxes of narrated objects for every annotated frame.
  • Actions: Split into narrations and action labels:
    • Narrations containing the narrated sentence with the timestamp.
    • Action labels containing the verb and noun labels along with the start and end times of the segment.

Dataset Splits

The dataset is comprised of three splits with the corresponding ground truth:

  • Training set - Full ground truth.
  • Seen Kitchens (S1) Test set - Start/end times only.
  • Unseen Kitchens (S2) Test set - Start/end times only.

Initially we are only releasing the full ground truth for the training set in order to run action and object challenges.

Important Files

We direct the reader to RDSF for the videos and rgb/flow frames.

We provide html and pdf alternatives to this README which are auto-generated.

Files Structure

EPIC_train_action_labels.csv

CSV file containing 14 columns:

Column Name Type Example Description
uid int 6374 Unique ID of the segment.
video_id string P03_01 Video the segment is in.
narration string close fridge English description of the action provided by the participant.
start_timestamp string 00:23:43.847 Start time in HH:mm:ss.SSS of the action.
stop_timestamp string 00:23:47.212 End time in HH:mm:ss.SSS of the action.
start_frame int 85430 Start frame of the action (WARNING only for frames extracted as detailed in Video Information).
stop_frame int 85643 End frame of the action (WARNING only for frames extracted as detailed in Video Information).
participant_id string P03 ID of the participant.
verb string close Parsed verb from the narration.
noun string fridge First parsed noun from the narration.
verb_class int 3 Numeric ID of the parsed verb's class.
noun_class int 10 Numeric ID of the parsed noun's class.
all_nouns list of string (1 or more) ['fridge'] List of all parsed nouns from the narration.
all_noun_classes list of int (1 or more) [10] List of numeric IDs corresponding to all of the parsed nouns' classes from the narration.

Please note we have included a python pickle file for ease of use. This includes a pandas dataframe with the same layout as above. This pickle file was created with pickle protocol 2 on pandas version 0.22.0.

EPIC_train_invalid_labels.csv

CSV file containing 14 columns:

Column Name Type Example Description
uid int 6374 Unique ID of the segment.
video_id string P03_01 Video the segment is in.
narration string close fridge English description of the action provided by the participant.
start_timestamp string 00:23:43.847 Start time in HH:mm:ss.SSS of the action.
stop_timestamp string 00:23:47.212 End time in HH:mm:ss.SSS of the action.
start_frame int 85430 Start frame of the action (WARNING only for frames extracted as detailed in Video Information).
stop_frame int 85643 End frame of the action (WARNING only for frames extracted as detailed in Video Information).
participant_id string P03 ID of the participant.
verb string close Parsed verb from the narration.
noun string fridge First parsed noun from the narration.
verb_class int 3 Numeric ID of the parsed verb's class.
noun_class int 10 Numeric ID of the parsed noun's class.
all_nouns list of string (1 or more) ['fridge'] List of all parsed nouns from the narration.
all_noun_classes list of int (1 or more) [10] List of numeric IDs corresponding to all of the parsed nouns' classes from the narration.

Please note we have included a python pickle file for ease of use. This includes a pandas dataframe with the same layout as above. This pickle file was created with pickle protocol 2 on pandas version 0.22.0.

EPIC_train_action_narrations.csv

CSV file containing 5 columns:

Note: The start/end timestamp refers to the start/end time of the narration, not the action itself.

Column Name Type Example Description
participant_id string P03 ID of the participant.
video_id string P03_01 Video the segment is in.
start_timestamp string 00:23:43.847 Start time in HH:mm:ss.SSS of the narration.
stop_timestamp string 00:23:47.212 End time in HH:mm:ss.SSS of the narration.
narration string close fridge English description of the action provided by the participant.

EPIC_train_object_labels.csv

CSV file containing 6 columns:

Column Name Type Example Description
noun_class int 20 Integer value representing the class in noun-classes.csv.
noun string bag Original string name for the object.
participant_id string P01 ID of participant.
video_id string P01_01 Video the object was annotated in.
frame int 056581 Frame number of the annotated object.
bounding_boxes list of 4-tuple (0 or more) "[(76, 1260, 462, 186)]" Annotated boxes with format (<top:int>,<left:int>,<height:int>,<width:int>).

EPIC_test_s1_timestamps.csv

CSV file containing 7 columns:

Column Name Type Example Description
uid int 1924 Unique ID of the segment.
participant_id string P01 ID of the participant.
video_id string P01_11 Video the segment is in.
start_timestamp string 00:00:00.000 Start time in HH:mm:ss.SSS of the action.
stop_timestamp string 00:00:01.890 End time in HH:mm:ss.SSS of the action.
start_frame int 1 Start frame of the action (WARNING only for frames extracted as detailed in Video Information).
stop_frame int 93 End frame of the action (WARNING only for frames extracted as detailed in Video Information).

Please note we have included a python pickle file for ease of use. This includes a pandas dataframe with the same layout as above. This pickle file was created with pickle protocol 2 on pandas version 0.22.0.

EPIC_test_s2_timestamps.csv

CSV file containing 7 columns:

Column Name Type Example Description
uid int 15582 Unique ID of the segment.
participant_id string P09 ID of the participant.
video_id string P09_01 Video the segment is in.
start_timestamp string 00:00:01.970 Start time in HH:mm:ss.SSS of the action.
stop_timestamp string 00:00:03.090 End time in HH:mm:ss.SSS of the action.
start_frame int 118 Start frame of the action (WARNING only for frames extracted as detailed in Video Information).
stop_frame int 185 End frame of the action (WARNING only for frames extracted as detailed in Video Information).

Please note we have included a python pickle file for ease of use. This includes a pandas dataframe with the same layout as above. This pickle file was created with pickle protocol 2 on pandas version 0.22.0.

EPIC_noun_classes.csv

CSV file containing 3 columns:

Note: a colon represents a compound noun with the more generic noun first. So pan:dust should be read as dust pan.

Column Name Type Example Description
noun_id int 2 ID of the noun class.
class_key string pan:dust Key of the noun class.
nouns list of string (1 or more) "['pan:dust', 'dustpan']" All nouns within the class (includes the key).

EPIC_verb_classes.csv

CSV file containing 3 columns:

Column Name Type Example Description
verb_id int 3 ID of the verb class.
class_key string close Key of the verb class.
verbs list of string (1 or more) "['close', 'close-off', 'shut']" All verbs within the class (includes the key).

EPIC_descriptions.csv

CSV file containing 4 columns:

Column Name Type Example Description
video_id string P01_01 ID of the video.
date string 30/04/2017 Date on which the video was shot.
time string 13:49:00 Local recording time of the video.
description string prepared breakfast with soy milk and cereals Description of the activities contained in the video.

EPIC_many_shot_verbs.csv

CSV file containing the many shot verbs. A verb class is considered many shot if it appears more than 100 times in training. (NOTE: this file is derived from EPIC_train_action_labels.csv, checkout the accompanying notebook demonstrating how we compute these classes)

Column Name Type Example Description
verb_class int 1 Numeric ID of the verb class
verb string put Verb corresponding to the verb class

EPIC_many_shot_nouns.csv

CSV file containing the many shot nouns. A noun class is considered many shot if it appears more than 100 times in training. (NOTE: this file is derived from EPIC_train_action_labels.csv, checkout the accompanying notebook demonstrating how we compute these classes)

Column Name Type Example Description
noun_class int 3 Numeric ID of the noun class
noun string tap Noun corresponding to the noun class

EPIC_many_shot_actions.csv

CSV file containing the many shot actions. An action class (composed of a verb class and noun class) is considered many shot if BOTH the verb class and noun class are many shot AND the action class appears in training at least once. (NOTE: this file is derived from EPIC_train_action_labels.csv, checkout the accompanying notebook demonstrating how we compute these classes)

Column Name Type Example Description
action_class (int, int) (9, 84) Numeric Pair of IDs, first the verb, then the noun
verb_class int 9 Numeric ID of the verb class
verb string move Verb corresponding to the verb class
noun_class int 84 Numeric ID of the noun class
noun string sausage Noun corresponding to the noun class

EPIC_video_info.csv

CSV file containing information for each video

Column Name Type Example Description
video (string) P01_01 Video ID
resolution (string) 1920x1080 Resolution of the video, format is WIDTHxHEIGHT
duration (float) 1652.152817 Duration of the video, in seconds
fps (float) 59.9400599400599 Frame rate of the video

File Downloads

Due to the size of the dataset we provide scripts for downloading parts of the dataset:

Note: These scripts will work for Linux and Mac. For Windows users a bash installation should work.

These scripts replicate the folder structure of the dataset release, found here.

If you wish to download part of the dataset instructions can be found here.

Video Information

Videos are recorded in 1080p at 59.94 FPS on a GoPro Hero 5 with linear field of view. There are a minority of videos which were shot at different resolutions, field of views, or FPS due to participant error or camera. These videos identified using ffprobe are:

  • 1280x720: P12_01, P12_02, P12_03, P12_04.
  • 2560x1440: P12_05, P12_06
  • 29.97 FPS: P09_07, P09_08, P10_01, P10_04, P11_01, P18_02, P18_03
  • 48 FPS: P17_01, P17_02, P17_03, P17_04
  • 90 FPS: P18_09

The GoPro Hero 5 was also set to drop the framerate in low light conditions to preserve exposure leading to variable FPS in some videos. If you wish to extract frames we suggest you resample at 60 FPS to mitigate issues with variable FPS, this can be achieved in a single step with FFmpeg:

ffmpeg -i "P##_**.MP4" -vf "scale=-2:256" -q:v 4 -r 60 "P##_**/frame_%010d.jpg"

where ## is the Participant ID and ** is the video ID.

Optical flow was extracted using a fork of gpu_flow made available on github. We set the parameters: stride = 2, dilation = 3, bound = 25 and size = 256.

License

All files in this dataset are copyright by us and published under the Creative Commons Attribution-NonCommerial 4.0 International License, found here. 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.

Changelog

  • 09/04/18: Initial Release