ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

• URL: http://arxiv.org/abs/2205.05177v1
• Date: Tue, 10 May 2022 21:30:10 GMT
• Title: ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild
• Authors: Chirag Raman, Jose Vargas-Quiros, Stephanie Tan, Ekin Gedik, Ashraful Islam, Hayley Hung
• Abstract summary: We describe an instantiation of a new concept for multimodal multisensor data collection of real life in-the-wild free standing social interactions. ConfLab contains high fidelity data of 49 people during a real-life professional networking event.
• Score: 10.686716372324096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe an instantiation of a new concept for multimodal multisensor data collection of real life in-the-wild free standing social interactions in the form of a Conference Living Lab (ConfLab). ConfLab contains high fidelity data of 49 people during a real-life professional networking event capturing a diverse mix of status, acquaintanceship, and networking motivations at an international conference. Recording such a dataset is challenging due to the delicate trade-off between participant privacy and fidelity of the data, and the technical and logistic challenges involved. We improve upon prior datasets in the fidelity of most of our modalities: 8-camera overhead setup, personal wearable sensors recording body motion (9-axis IMU), Bluetooth-based proximity, and low-frequency audio. Additionally, we use a state-of-the-art hardware synchronization solution and time-efficient continuous technique for annotating body keypoints and actions at high frequencies. We argue that our improvements are essential for a deeper study of interaction dynamics at finer time scales. Our research tasks showcase some of the open challenges related to in-the-wild privacy-preserving social data analysis: keypoints detection from overhead camera views, skeleton based no-audio speaker detection, and F-formation detection. With the ConfLab dataset, we aim to bridge the gap between traditional computer vision tasks and in-the-wild ecologically valid socially-motivated tasks.

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