An Occupant Sensing System Using Single Video Camera and Ultrasonic Sensor for Advanced Airbag

단일 비디오 카메라와 초음파센서를 이용한 스마트 에어백용 승객 감지 시스템

  • 배태욱 (경북대학교 전자전기컴퓨터 공학부) ;
  • 이종원 (경북대학교 전자전기컴퓨터 공학부) ;
  • 하수영 (경북대학교 전자전기컴퓨터 공학부) ;
  • 김영춘 (영동대학교 정보통신사이버경찰학과) ;
  • 안상호 (인제대학교 전자지능로봇공학과) ;
  • 송규익 (경북대학교 전자전기컴퓨터학부)
  • Received : 2009.06.26
  • Accepted : 2009.09.08
  • Published : 2010.01.30

Abstract

We proposed an occupant sensing system using single video camera and ultrasonic sensor for the advanced airbag. To detect the occupant form and the face position in real-time, we used the skin color and motion information. We made the candidate face block image using the threshold value of the color difference signal corresponding to skin color and difference value of current image and previous image of luminance signal to gel motion information. And then it detects the face by the morphology and the labeling. In case of night without color and luminance information, it detects the face by using the threshold value of the luminance signal get by infra-red LED instead of the color difference signal. To evaluate the performance of the proposed occupant detection system, it performed various experiments through the setting of the IEEE camera, ultrasonic sensor, and infra-red LED in vehicle jig.

본 논문에서는 단일 비디오카메라와 초음파센서를 이용한 스마트 에어백용 승객 감지 시스템을 제안하였다. 승객의 체형과 얼굴 위치를 검출하기 위하여, 실시간 검출이 용이한 얼굴색 및 움직임 정보를 이용한다. 비디오 카메라 영상에서 얼굴색에 해당하는 색차신호 (U/V)의 경계값과 휘도신호 (Y)의 현재 프레임과 이전 프레임간의 차이값을 이용하여 후보 얼굴 블록 영상을 만든 후 모폴로지 및 라벨링 과정을 거쳐 얼굴 위치를 검출한다. 제안한 승객 자세감지 시스템의 성능을 평가하기 위하여 차량 지그에 IEEE 카메라, 초음파 센서 및 적외선 LED를 설치하여 다양한 실험을 수행하였다.

Keywords

References

  1. http://www.safeny.com/seat-ndx.htm.
  2. National Highway Transportation and Safety Administration, http://www.safercar.gov.
  3. National Highway Transportation and Safety Administration, Fatality Reduction by Airbags, Analysis of Accident Data Through Early 1996.
  4. P.A. Dunn and P.I. Corke, "Real-Time Stereopsis Using FPGAs," In Proc. Int. Workshop on Field Programmable Logic, 1mperial College, London, Vol.1304, pp. 400-409, 1997.
  5. R. Kjeldsen and J. Kender, "Finding Skin in Color Images," Proc. Conf. Automatic face and Gesture Recognition, pp. 312-317, 1996.
  6. S.K. Singh, D.S. Chauhan, M. Vatsa, and R Singh, "A Robust Skin Color Based Face Detection Algorithm," Tamkang Journal of Science and Engineering, Vol.6, No.4, pp. 227-234, 2003.
  7. I. Craw, H. Ellis, and J. Lishman, "Automatic Extraction of Face Features," Pattern Recognition Letters, Vol.5, No.2, pp. 183-187, 1987. https://doi.org/10.1016/0167-8655(87)90039-0
  8. P. Viola and M. Jones, "Robust Real-Time Face Detection," Intl. of Computer Vision, Vol.57, No.2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  9. M. Turk and A. pentland, "Face Recognition Using Eigenfaces," IEEE. Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.
  10. X, Zhang, J. Pu, and X. Huang, "Face Detection Based on Two Dimensional Principal Component Analysis and Support Vector Mach.ine," Proc. IEEE. Mechatronics and Automation, pp. 1488-1492, 2006.
  11. C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol.2, pp. 121-167, 1998. https://doi.org/10.1023/A:1009715923555
  12. C. Shavers, R. Li, and G. Lebby, "A SVM-Based Approach to Face Detection," Proc. of the 38th Southeastern Symposium on System Theory, pp. 362-366, 2006.
  13. http//www.safercar.gov/air.htm.
  14. National Highway Transportation and Safety Administration, "Occupant Crash Protection Standard," Federal Motor Vehicle Safety Standards FMVSS No. 208, 2002.