Abstract
This paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. The CultureMoves’ “Motion-Notes” Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. This prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.
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Acknowledgements
This work was supported by the project CultureMoves, Grant Agreement Number: INEA/CEF/ICT/A2017/1568369, Action No: 2017-EU-tA-0171.
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Rodrigues, R., Madeira, R.N., Correia, N., Fernandes, C., Ribeiro, S. (2019). Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_3
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