“Deep-ChildAR bot: Educational Activities and Safety Care Augmented Reality system with Deep-learning for Preschool” by Park, Ro and Han

  • ©Yoon Jung Park, Hyocheol Ro, and Tack-Don Han

  • ©Yoon Jung Park, Hyocheol Ro, and Tack-Don Han

Conference:


Entry Number: 26

Title:

    Deep-ChildAR bot: Educational Activities and Safety Care Augmented Reality system with Deep-learning for Preschool

Presenter(s):



Abstract:


    We propose a projection-based augmented reality (AR) robot system that provides pervasive support for the education and safety of preschoolers via a deep learning framework. This system can utilize real-world objects as metaphors for educational tools by performing object detection based on deep learning in real-time, and it can help recognize the dangers of real-world objects that may pose risks to children. We designed the system in a simple and intuitive way to provide user-friendly interfaces and interactions for children. Children’s experiences through the proposed system can improve their physical, cognitive, emotional, and thinking abilities.

References:


    • Ajaya Kumar Dash, Santosh Kumar Behera, Debi Prosad Dogra, and Partha Pratim Roy. 2018. Designing of marker-based augmented reality learning environments for kids using convolutional neural network architecture. Displays 55 (2018), 46–54. 
    • Joseph Redmon. 2013–2016. Darknet: Open Source Neural Networks in C. http: //pjreddie.com/darknet/. 
    • Thomas Whelan, Michael Kaess, Hordur Johannsson, Maurice Fallon, John J Leonard, and John McDonald. 2015. Real-time large-scale dense RGB-D SLAM with volumetric fusion. The International Journal of Robotics Research 34, 4-5 (2015), 598–626. 
    • Andrew D Wilson. 2010. Using a depth camera as a touch sensor. In ACM international conference on interactive tabletops and surfaces. ACM, 69–72. 
    • Hsin-Kai Wu, Silvia Wen-Yu Lee, Hsin-Yi Chang, and Jyh-Chong Liang. 2013. Current status, opportunities and challenges of augmented reality in education. Computers & education 62 (2013), 41–49.

Keyword(s):



Acknowledgements:


    This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No.NRF2018R1A2A1A05078628).


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