The doi above is for this specific version of this collection, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/c.6034313

Raman, Chirag; Vargas Quiros, Jose; Tan, Stephanie; Islam, Ashraful; Gedik, Ekin et. al. (2022): ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild. Version 4. 4TU.ResearchData. collection. https://doi.org/10.4121/c.6034313.v4

Collection

x choose version: version 3 - 2022-10-07 version 2 - 2022-06-20 version 1 - 2022-06-09

• Artificial Intelligence and Image Processing

audio, ConfLab, conflab, dataset, multimodal, video, wearables

by Chirag Raman , Jose Vargas Quiros , Stephanie Tan , Ashraful Islam , Ekin Gedik , Hayley Hung

ConfLab is a multimodal multisensor dataset of in-the-wild free-standing social conversations. It records a real-life professional networking event at the international conference ACM Multimedia 2019. Involving 48 conference attendees, the dataset captures a diverse mix of status, acquaintance, and networking motivations. Our capture setup improves upon the data fidelity of prior in-the-wild datasets while : 8 overhead-perspective videos (1920 x 1080, 60fps), and custom personal wearable sensors with onboard recording of body motion (full 9-axis IMU), privacy-preserving low-frequency audio (1250Hz), and Bluetooth-based proximity. Additionally, we developed custom solutions for distributed hardware synchronization at acquisition, and time-efficient continuous annotation of body keypoints and actions at high sampling rates. 

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General information: 

The dataset contains:

1. EULA: (DOI: 10.4121/20016194, required for access): End-User License Agreement needs to be filled out in order to request access because data contains pseudonymized data. Once completed, please return to SPCLabDatasets-insy@tudelft.nl. Private links to download the data will be sent to you when your credentials are reviewed and approved. Note for reviewers: please follow the same procedure described above. TUDelft Human Research Ethics Committee or a member of the Admin staff will handle your access requests for the review period to ensure single blind standard. 
2. Datasheet (DOI: 10.4121/20017559): data sheet summary of the ConfLab Data
3. Samples (DOI: 10.4121/20017682): sample data of the ConfLab dataset 
4. Raw-Data (DOI: 10.4121/20017748): raw video and wearable sensor data of the ConfLab dataset. 
5. Processed-Data (DOI: 10.4121/20017805): processed video and wearable sensor data of the ConfLab dataset. Used for annotations and processed for usability. 
6. Annotations (DOI: 10.4121/20017664): annotations of pose, speaking status, and F-formations. 

Please scroll down the page to see and access all the components in the ConfLab dataset. For more information, please see the respective readme.

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Baseline tasks

Baseline tasks include: keypoint pose estimation, speaking status estimation, and F-formation (conversation group) estimation. 

Code related to the baseline tasks can be found here:  

https://github.com/TUDelft-SPC-Lab/conflab

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Annotation tool

The annotation tool developed and used for annotating keypoints and speaking status of the ConfLab dataset, is provided here: https://github.com/josedvq/covfee

More information can be found here: Quiros, Jose Vargas, et al. "Covfee: an extensible web framework for continuous-time annotation of human behavior." Understanding Social Behavior in Dyadic and Small Group Interactions. PMLR, 2022.

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Wearable sensor (Midge) hardware

The wearable sensor developed and used for collecting data for the ConfLab dataset. More details can be found: https://github.com/TUDelft-SPC-Lab/spcl_midge_hardware

Please contact SPCLabDatasets-insy@tudelft.nl if you have any inquiries.  

• 2022-06-09 first online

• 2022-10-10 published, posted, revised

4TU.ResearchData

• NWO 639.022.606.
• Aspasia grant associated with vidi grant 639.022.606.

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Intelligent Systems