융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
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딥러닝 기반의 사용자인증을 활용한 어린이 버스에서 안전한 승차 및 하차 시스템 설계

Design for Safety System get On or Off the Kindergarten Bus using User Authentication based on Deep-learning

  • 문형진 (성결대학교 정보통신공학과)
  • Mun, Hyung-Jin (Dept. of Information and Communication Engineering, Sungkyul University)
  • 투고 : 2020.04.05
  • 심사 : 2020.05.20
  • 발행 : 2020.05.28
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초록

최근 어린이 차량의 승하차 과정에서 어린이 안전사고가 발생한다. 차량 인솔 교사가 없는 경우 버스에서 하차하지 않은 어린이의 질식사나 차량 전후방의 사각지대의 어린이 안전사고가 빈번하게 발생한다. 딥러닝 기반의 얼굴인식기술을 스마트 미러에 적용하여 사용자인증의 활용시 안전사고 방지를 위한 서비스가 가능하다. 스마트미러는 어린이를 위한 도우미 역할이 가능하고, 운전기사나 선생님이 미처 발견하지 못해 발생 가능할 사고를 방지할 수 있다. 어린이의 얼굴을 사전에 등록하여 어린이의 승하차시에 사용자인증을 수행하여 누락되지 않고, 버스의 전후방에 근접센서 및 카메라를 통해 안전사고를 미연에 방지할 수 있다. 본 연구는 어린이의 버스 승하차 과정에서 누락여부를 확인하고, 차량 전후방의 사각지대를 줄일 수 있는 시스템을 설계하고, GPS 정보를 활용하여 다양한 서비스가 가능한 안전시스템을 제안한다.

Recently, many safety accidents involving children shuttle buses take place. Without a teacher for help, a safety accident occurs when the driver can't see a child who is getting off in the blind spot of both frontside and backside. A deep learning-based smart mirror allows user authentication and provides various services. Especially, It can be a role of helper for children, and prevent accidents that can occur when drivers or assistant teachers do not see them. User authentication is carried out with children's face registered in advance. Safety accidents can be prevented by an approximate sensor and a camera in frontside and backside of the bus. This study suggests a way of checking out whether children are missed in the process of getting in and out of the bus, designs a system that reduce blind spots in the front and back of the vehicle, and builds a safety system that provide various services using GPS.

키워드

참고문헌

  1. H. Ko, S. Kim & N. Kang. (2017). Design and implementation of smart-mirror supporting recommendation service based on personal usage data. KIISE transactions on computing practices, 23(1), 65-73. https://doi.org/10.5626/KTCP.2017.23.1.65
  2. H. J. Mun. (2019). A Study on the User Identification and Authentication in the Smart Mirror in Private. Journal of Convergence for Information Technology, 9(7), 100-105. DOI : 10.22156/CS4SMB.2019.9.7.100
  3. Eben Upton. (2019). Raspberry Pi 4 on sale now from $35. Raspberry(Online). https://www.raspberrypi.org/blog/raspberrypi-4-on-sale-now-from-35/
  4. S. W. Lee, D. M. Ji, H. S. Shin, Y. B. Chae & Y. G. Kim. (2018). A Personalised Smart Mirror Based on Face Recognition. Proc. of Korea Information Science Society, 1644-1646.
  5. P. Y. Kumbhar, A. Mulla, P. Kanagi & R. Shah. (2018). Smart Mirror Using Raspberry PI. International Journal for Research in Emerging Science and Technology, 5(4), 2349-2610.
  6. Raspbian. Welcome to Raspbian. https://www.raspbian.org/
  7. W. Harrington. (2015). Learning Raspbian. Packt Publishing Ltd.
  8. H. J. Mun. (2018). Biometric Information and OTP based on Authentication Mechanism using Blockchain. Journal of Convergence for Information Technology, 8(3), 85-90. DOI : 10.22156/CS4SMB.2018.8.3.085
  9. J. Shin, Z. Liu, C. M. Kim & H. J. Mun. (2018). Writer identification using intra-stroke and inter-stroke information for security enhancements in P2P systems. Peer-to-Peer Networking and Applications, 11(6), 1166-1175. DOI : 10.1007/s12083-017-0606-0
  10. J. Shin, M. R. Islam, M. A. Rahim & H. J. Mun. (2020). Arm movement activity based user authentication in P2P systems. Peer-to-Peer Networking and Applications, 13, 635-646 (2020). Issue Date March 2020. DOI : 10.1007/s12083-019-00775-7 Springer US.
  11. S. V. Viraktamath, M. Katti, A. Khatawkar & P. Kulkarni. (2013). Face detection and tracking using OpenCV. The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), 1(3), 45-50.
  12. Y. C. Hwang, H. J. Mun & J. W. Lee. (2015). Face Recognition System Technologies for Authentication System-A Survey. Journal of Convergence for Information Technology, 5(3), 9-13. DOI : 10.22156/CS4SMB.2015.5.3.009.
  13. Face detection.(2020). An AI service that analyzes faces in images. Microsoft Azure(Online). https://azure.microsoft.com/en-us/services/cognitive-services/face
  14. H. J. Mun & G. H. Kim. (2019). A Survey on Deep Learning based Face Recognition for User Authentication. Journal of Industrial Convergence, 17(3), 23-29. DOI : 10.22678/JIC.2019.17.3.023
  15. O. Abdel-Hamid, A. R. Mohamed, H. Jiang, L. Deng, G. Penn & D. Yu. (2014). Convolutional neural networks for speech recognition. IEEE/ACM Transactions on audio, speech, and language processing, 22(10), 1533-1545. https://doi.org/10.1109/TASLP.2014.2339736
  16. T. Kimura, T. Nose, S. Hirooka, Y. Chiba & A. Ito. (2018, November). Comparison of Speech Recognition Performance Between Kaldi and Google Cloud Speech API. In International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 109-115). Springer, Cham.