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egocentric 4d perception ego4d video is not supported in this browser a massive scale egocentric dataset and benchmark suite collected across 74 worldwide locations and 9 countries with over 3 670 hours of daily life activity video tap hover over map markers above and wait for sample video to load tap to start stop the map from moving explore explore sample nearr watch video nearr challenges nearr start here nearr start here benchmarks challenges consortium q a team download massive scale explore sample nearr ego4d is a massive scale egocentric dataset of unprecedented diversity it consists of 3 670 hours of video collected by 923 unique participants from 74 worldwide locations in 9 different countries the project brings together 88 researchers in an international consortium to dramatically increases the scale of egocentric data publicly available by an order of magnitude making it more than 20x greater than any other data set in terms of hours of footage ego4d aims to catalyse the next era of research in first person visual perception diverse the dataset is diverse in its geographic coverage scenarios participants and captured modalities we consulted a survey from the u s bureau of labor statistics that captures how people spend the bulk of their time data was captured using seven different off the shelf head mounted cameras gopro vuzix blade pupil labs zshades or dro ep6 ivue rincon 1080 and weeview in addition to video portions of ego4d offer other data modalities 3d scans audio gaze stereo multiple synchronized wearable cameras and textual narrations check our arxiv paper for details privacy ethics from the onset privacy and ethics standards were critical to this data collection effort each partner was responsible for developing a policy while specifics vary per site common guidelines have been followed please refer to our statement by members of the consortium on issuess of privacy and ethics for further details should any researcher participant or data user encounter clips they have been insufficiently redacted or for any other privacy concerns please contact us immediately at privacy ego4d data org previous next benchmarks episodic memory hoi av diarization social forecasting episodic memory where is my x egocentric video gives a recording of a wearer s daily life and can be used augment human memory on demand such a system might be able to remind a user where they left their keys if they added salt to a recipe or recall events they attended querying memory there are three different tasks within this benchmark based on the input type used to query the memory visual query i e find the location given an image of keys textual query how many cups of sugar did i add and a moment query find all instances of when did i play with the dog construction of queries for the language queries a set of templates were designed which annotators used to write questions for the task examples include what is the state of object x or where is object x after event y these were then re written for variety recalling lives given the broad nature of this benchmark there isn t a subset of activities that were focused on within this task leading to a realistic and challenging benchmark hand object interaction how do objects change during interactions going beyond action recognition this benchmark follows when where and how an object is changed during its interaction only possible through a first person viewpoint changes of state we capture annotations of objects as they transform temporally spatially and semantically an onion might be minced these are represented by three different tasks in the benchmark point of no return temporal localisation active object detection and state change classification pre post conditions each annotation has been labelled with prior states i e the prior condition and posterior states as well as the point of no return pnr in which the state change is triggered world of interactions the data for this challenge has been selected from activities with a high level of hand object interactions such as knitting carpentry and baking social interactions who is attending to whom an egocentric video provides a unique lens for studying social interactions because it captures utterances and nonverbal cues from each participant s unique view and enables embodied approaches to social understanding more than conversation social extends the audio visual diaraization benchmark towards understanding the conversations of a social group over a longer period of time for specific tasks talking and listening this benchmark includes two different tasks focused on when a person is looking at me and when a person is talking to me unique interactions the data within the social interaction task was collected specifically for this task in mind with multi user scenarios such as social deduction games eating drinking and playing basketball audio visual diarization who said what and when conversations are egocentric in nature and a human in the loop ai requires skills such as localizing a speaker and transcribing speech content looking for conversation this benchmark contains 2 different tasks focused on visual data localizing and tracking of the speakers in the visual field of view note that identities are anonymized to match consortium guidelines hearing the words the benchmark also includes 2 tasks for the audio modality diaraization temporal extent of the sentences spoken and the transcription of the conversation much ado about talking with this task focused on conversations scenarios were chosen which included multiple participants interacting together such as eating playing games or setting up tents forecasting predicting the future is a critical skill for ai systems to provide timely assistance for users with a myriad of long form unscripted videos ego4d provides an interesting challenge for different forecasting tasks where will i move two tasks consider the future motion of the user with hands and feet models should predict where the camera wearer will go within the scene and the future location of wearer s hands what will happen next two tasks consider short and long term future anticipation algorithms should be able to predict the next object interaction that will take place and a countdown towards it taking place as well as the long term what are the next possible sequence of actions data for prophets the data for this challenge has been selected from a diverse set of activities containing many human object interactions and movements such as brick making cooking or carpentry challenges episodic memory hand object interactions av diarization social forecasting ego4d consortium this initiative is led by an international consortium of 13 universities in partnership with facebook ai that collaborated to advance egocentric perception questions answers what to cite referencing this effort if using the dataset annotations or inspiration from this work cite arxiv paper k grauman et al ego4d around the world in 3 000 hours of egocentric video arxiv preprint arxiv 2110 07058 2021 bibtex bib how can i download the dataset ego4d is now publicly avaiable obtaining the dataset or any annotations requires you first review our license agreement and accept the terms go here to review and execute this agreement and you will be emailed a set of aws access credentials when your license agreement is approved which will take 48hrs you can review a draft of the licenses before signing here in the meantime you can check out data overview and sample notebooks here to get familiar with the dataset and can download the cli and dataloaders to get setup in advance what metadata is available for each video we provide information about the collecting partner university date of recording recording equipment as well as video parts when the video is made up of smaller chunks information about the availability of imu audio and whether videos have been redacted are also included overviews of the metadata and annotations can be found in our docs who collected this data the data was collected from 923 participants we showcase a distribution of age gender and jobs from around 70 of our participants who volunteered to self identify their demographics age gender countries of residence and occupations does the data contain identifying information of individuals the collecting partner holds consent forms and or release forms for all videos only when consent has been collected from participants the data will contain faces and other identifying information for the majority of videos data has been de identified pre release refer to our privacy statement and arxiv sec 3 4 and appendix c for details of our privacy and de identification pipeline what coverage of scenarios do you have a sample visualisation of our scenarios is below outer circle shows the 14 most common scenarios 70 of the data wordle shows scenarios in the remaining 30 inner circle is color coded by the contributing partner see map marker above do you offer pre extracted features yes we provide precomputed action video features for the full dataset and plan other features you can find details and download these features from here what equipment resolution and frame rate are available this depends on equipment to avoid models overfitting to a single capture device seven different head mounted cameras were deployed across the dataset gopro vuzix blade pupil labs zshades ordro ep6 ivue rincon 1080 and weeview we release all footage using the native resolution but also offer a standardised frame rate version of 30fps for ease of use all benchmark results use the standardised version how can i participate in the benchmarks the first round of challenges are open now please see the challenge documentation here results will be announced at the joint 1st ego4d workshop in conjunction with 10th epic workshop alongside cvpr 2022 ego4d team carnegie mellon university pittsburgh u s kris kitani pi xingyu liu qichen fu sean crane xuhua huang xindi wu carnegie mellon university africa rawanda abrham gebreselasie king abdullah university of science and technology ksa bernard ghanem pi chen zhao mengmeng xu merey ramazanova university of minnesota u s hyun soo park pi jayant sharma tien do zachary chavis international institute of information technology hyderabad india c v jawahar pi raghava modhugu siddhant bansal indiana university bloomington u s david crandall pi yuchen wang weslie khoo university of pennsylvania u s jianbo shi pi university of catania italy giovanni maria farinella pi antonino furnari university of tokyo japan yoichi sato pi takuma yagi takumi nishiyasu yifei huang yusuke sugano zhenqiang li facebook ai research international kristen grauman pi jitendra malik pi dhruv batra eugene byrne vincent cartillier morrie doulaty akshay erapalli christian fuegen rohit girdhar jackson hamburger tal hassner james hillis frl vamsi krishna ithapu frl hao jiang hanbyul joo jachym kolar satwik kottur devansh kukreja anurag kumar frl federico landini chao li frl miguel martin tullie murrell tushar nagarajan christoph feichtenhofer karttikeya mangalam richard newcombe frl santhosh kumar ramakrishnan leda sari frl kiran somasundaram frl lorenzo torresani minh vo frl andrew westbury mingfei yan frl university of bristol uk dima damen pi michael wray will price jonathan munro adriano fragomeni national university of singapore singapore mike zheng shou pi haizhou li co pi eric z xu ruijie tao yunyi zhu georgia institute of technology u s jim rehg pi miao liu fiona ryan audrey southerland wenqi jia universidad de los andes colombia pablo arbelaez pi cristina gonzalez paola ruiz puentes massachusetts institute of technology u s aude oliva pi antonio torralba pi others ilija radosavovic uc berkeley download ego4d the dataset was released 17 feb 2022 and is now publicly available license forms should be signed to access videos metadata and annotations sign ego4d licenses nearr starting code and readme files are avaiable start here nearr contact ego4d email us at info ego4d data org