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  • Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

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    Sugianto505106-Accepted.pdf (1.530Mb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Sugianto, Nehemia
    Tjondronegoro, Dian
    Stockdale, Rosemary
    Yuwono, Elizabeth Irenne
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Stockdale, Rosemary
    Sugianto, Nehemia
    Yuwono, Irenne I.
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Purpose: The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces. Design/methodology/approach: The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to ...
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    Purpose: The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces. Design/methodology/approach: The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server. Findings: The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence. Originality/value: The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.
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    Journal Title
    Information Technology & People
    DOI
    https://doi.org/10.1108/ITP-07-2020-0534
    Copyright Statement
    © 2021 Emerald. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Artificial intelligence
    Sociology and social studies of science and technology
    Science & Technology
    Information Science & Library Science
    Artificial intelligence
    Responsible AI
    Publication URI
    http://hdl.handle.net/10072/407130
    Collection
    • Journal articles

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