Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

Peng, Xiangjun and Huang, Zhentao and Sun, Xu (2020) Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research. In: ACM CHI Conference on Human Factors in Computing Systems, 25-30 April 2020, Honolulu, HI, USA. (Unpublished)

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Abstract

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN 9781450368193
Keywords: Human-VehicleInteractions; ComputerVision; Ergonomics
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Mechanical, Materials and Manufacturing Engineering
Related URLs:
URLURL Type
https://www.researchgate.net/publication/338804779_Building_BROOK_A_Multi-modal_and_Facial_Video_Database_for_Human-Vehicle_Interaction_ResearchUNSPECIFIED
Depositing User: Wu, Cocoa
Date Deposited: 21 May 2020 06:23
Last Modified: 21 May 2020 07:02
URI: http://eprints.nottingham.ac.uk/id/eprint/60664

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