Nehemia Sugianto, D. Tjondronegoro, R. Stockdale, E. Yuwono
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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.FindingsThe 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/valueThe 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.","PeriodicalId":47740,"journal":{"name":"Information Technology & People","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces\",\"authors\":\"Nehemia Sugianto, D. Tjondronegoro, R. Stockdale, E. 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Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces
PurposeThe paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.Design/methodology/approachThe 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.FindingsThe 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/valueThe 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.
期刊介绍:
Information Technology & People publishes work that is dedicated to understanding the implications of information technology as a tool, resource and format for people in their daily work in organizations. Impact on performance is part of this, since it is essential to the well being of employees and organizations alike. Contributions to the journal include case studies, comparative theory, and quantitative research, as well as inquiries into systems development methods and practice.