{"data":{"id":"10.4121/20017664","type":"dois","attributes":{"doi":"10.4121/20017664","prefix":"10.4121","suffix":"20017664","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Raman, Chirag","nameType":"Personal","givenName":"Chirag","familyName":"Raman","nameIdentifiers":[{"nameIdentifier":"0000-0003-4894-4206","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Vargas Quiros, Jose","givenName":"Jose","familyName":"Vargas Quiros","affiliation":[],"nameIdentifiers":[]},{"name":"Tan, Stephanie","givenName":"Stephanie","familyName":"Tan","affiliation":[],"nameIdentifiers":[]},{"name":"Islam, Ashraful","givenName":"Ashraful","familyName":"Islam","affiliation":[],"nameIdentifiers":[]},{"name":"Gedik, Ekin","givenName":"Ekin","familyName":"Gedik","affiliation":[],"nameIdentifiers":[]},{"name":"Hung, Hayley","givenName":"Hayley","familyName":"Hung","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Annotations for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2022,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"annotations"},{"subject":"conflab"},{"subject":"pose"},{"subject":"actions"},{"subject":"f-formations"},{"subject":"multimodal"},{"subject":"ConfLab"}],"contributors":[{"name":"TU Delft, Faculty Of Electrical Engineering, Mathematics And Computer Science, Intelligent Systems","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2022-10-10","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["zipped files"],"version":"3","rightsList":[{"rights":"Restrictive Licence","rightsUri":"https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf"}],"descriptions":[{"description":"This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ ./actions/speaking_status: ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) \u003cbr\u003e ./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- ./pose: ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- ./f_formations: seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: \"()\" delineates groups, \"\u0026lt;\u0026gt;\" delineates subjects, cam X indicates the best camera view for which a particular group exists. \u003cbr\u003e phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NWO 639.022.606.","funderName":"unknown"},{"awardTitle":"Aspasia grant associated with vidi grant 639.022.606.","funderName":"unknown"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.4121/20017664</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Chirag Raman</creatorName>
      <givenName>Chirag</givenName>
      <familyName>Raman</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0000-0003-4894-4206</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Jose Vargas Quiros</creatorName>
      <givenName>Jose</givenName>
      <familyName>Vargas Quiros</familyName>
    </creator>
    <creator>
      <creatorName>Stephanie Tan</creatorName>
      <givenName>Stephanie</givenName>
      <familyName>Tan</familyName>
    </creator>
    <creator>
      <creatorName>Ashraful Islam</creatorName>
      <givenName>Ashraful</givenName>
      <familyName>Islam</familyName>
    </creator>
    <creator>
      <creatorName>Ekin Gedik</creatorName>
      <givenName>Ekin</givenName>
      <familyName>Gedik</familyName>
    </creator>
    <creator>
      <creatorName>Hayley Hung</creatorName>
      <givenName>Hayley</givenName>
      <familyName>Hung</familyName>
    </creator>
  </creators>
  <titles>
    <title>Annotations for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild</title>
  </titles>
  <publisher>4TU.ResearchData</publisher>
  <publicationYear>2022</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
  <subjects>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0801">Artificial Intelligence and Image Processing</subject>
    <subject>annotations</subject>
    <subject>conflab</subject>
    <subject>pose</subject>
    <subject>actions</subject>
    <subject>f-formations</subject>
    <subject>multimodal</subject>
    <subject>ConfLab</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Intelligent Systems</contributorName>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2022-10-10</date>
  </dates>
  <formats>
    <format>zipped files</format>
  </formats>
  <version>3</version>
  <rightsList>
    <rights rightsURI="https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf">Restrictive Licence</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. &lt;/p&gt;
&lt;p&gt;------------------&lt;/p&gt;
&lt;p&gt;./actions/speaking_status:&lt;/p&gt;
&lt;p&gt;./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at:  https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status&lt;/p&gt;
&lt;p&gt;    The processed annotations consist of:&lt;/p&gt;
&lt;p&gt;        ./speaking: The first row contains person IDs matching the sensor IDs,&lt;/p&gt;
&lt;p&gt;        The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames).&lt;/p&gt;
&lt;p&gt;        ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation.&lt;/p&gt;
&lt;p&gt;To load these files with pandas: pd.read_csv(p, index_col=False)&lt;/p&gt;
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
&lt;p&gt;./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee)&lt;/p&gt;
&lt;p&gt;Annotations were done at 60 fps.&lt;/p&gt;
&lt;p&gt;--------------------&lt;/p&gt;
&lt;p&gt;./pose:&lt;/p&gt;
&lt;p&gt;./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints&lt;/p&gt;
&lt;p&gt;    To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json'))&lt;/p&gt;
&lt;p&gt;    The skeleton structure (limbs) is contained within each file in:&lt;/p&gt;
&lt;p&gt;        f['categories'][0]['skeleton']&lt;/p&gt;
&lt;p&gt;    and keypoint names at:&lt;/p&gt;
&lt;p&gt;        f['categories'][0]['keypoints']&lt;/p&gt;
&lt;p&gt;./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee)&lt;/p&gt;
&lt;p&gt;    Annotations were done at 60 fps.&lt;/p&gt;
&lt;p&gt;---------------------&lt;/p&gt;
&lt;p&gt;./f_formations:&lt;/p&gt;
&lt;p&gt;seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). &lt;/p&gt;
&lt;p&gt;seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). &lt;/p&gt;
&lt;p&gt;Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. &lt;/p&gt;
&lt;p&gt;First column: time stamp&lt;/p&gt;
&lt;p&gt;Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists.&lt;/p&gt;
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
&lt;p&gt;phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone&lt;/p&gt;</description>
  </descriptions>
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    <fundingReference>
      <funderName>unknown</funderName>
      <awardTitle>NWO 639.022.606.</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>unknown</funderName>
      <awardTitle>Aspasia grant associated with vidi grant 639.022.606.</awardTitle>
    </fundingReference>
  </fundingReferences>
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