“Context-aware Risk Degree Prediction for Smartphone Zombies” by Liu, Koike, Liao and Wu

  • ©Ruofan Liu, Hideki Koike, Chen-Chieh Liao, and Erwin Wu



Entry Number: 48


    Context-aware Risk Degree Prediction for Smartphone Zombies



    Using smartphones while walking is becoming a social problem. Recent works try to support this issue by different warning systems. However, most only focus on detecting obstacles, without considering the risk to the user. In this paper, we propose a deep learning-based context-aware risk prediction system using a built-in camera on smartphones, aiming to notify ”smombies” by a risk-degree based algorithm. The proposed system both estimates the risk degree of a potential obstacle and the user’s status, which can also be used for distracted driving or visually impaired people.


    Hwarang Goh, Woojeong Kim, Jaeho Han, Kyungsik Han, and Youngtae Noh. 2020. Smombie Forecaster: Alerting Smartphone Users About Potential Hazards in Their Surroundings. IEEE Access 8(2020), 153183–153191.Google ScholarCross Ref
    Donghee Kim, Kyungsik Han, Jeong Seop Sim, and Youngtae Noh. 2018. Smombie Guardian: We watch for potential obstacles while you are walking and conducting smartphone activities. PLoS one 13, 6 (2018), e0197050.Google ScholarCross Ref
    Erwin Wu and Hideki Koike. 2019. FuturePose – Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). 1384–1392. https://doi.org/10.1109/WACV.2019.00152Google ScholarCross Ref
    Takuma Yagi, Karttikeya Mangalam, Ryo Yonetani, and Yoichi Sato. 2018. Future Person Localization in First-Person Videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google ScholarCross Ref

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