Abstract
In high-end hospitality industries such as airline lounges, high star hotels, and high-class restaurants, employee service skills play an important role as an element of the brand identity. However, it is very difficult to train an intermediate employee into an expert employee who can provide higher value services which exceed customers’ expectations. To hire and develop employees who embody the value of the brand, it is necessary to clearly communicate the value of the brand to their employees. In the video analysis domain, especially analyzing human behaviors, an important task is the understanding and representation of human activities such as conversation, physical actions and their connections on the time. This paper addresses the problem of massively annotating video contents such as multimedia training materials, which then can be processed by human-interaction training support systems (such as VR training systems) as resources for content generation. In this paper, we propose a POC (proof of concept) system of a service skill assessing platform, which is a knowledge graph (KG) of high-end service provision videos massively annotated with human interaction semantics.
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Acknowledgments
Part of this work was supported by Council for Science, Technology and Innovation, “Cross-ministerial Strategic Innovation Promotion Program (SIP), Big-data and AI-enabled Cyberspace Technologies” (funding agency: NEDO).
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Fukuda, K., Vizcarra, J., Nishimura, S. (2020). Massive Semantic Video Annotation in High-End Customer Service. In: Nah, FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2020. Lecture Notes in Computer Science(), vol 12204. Springer, Cham. https://doi.org/10.1007/978-3-030-50341-3_4
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