DOI QR코드

DOI QR Code

Vehicle Detection for Adaptive Head-Lamp Control of Night Vision System

적응형 헤드 램프 컨트롤을 위한 야간 차량 인식

  • 김현구 (영남대학교 정보통신학과) ;
  • 정호열 (영남대학교 정보통신학과) ;
  • 박주현 (영남대학교 전기공학과)
  • Received : 2010.09.24
  • Accepted : 2010.12.08
  • Published : 2011.02.28

Abstract

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.

Keywords

References

  1. Y.M. Chan, S.S. Huang, L.c. Fu, and P.Y. Hsiao, "Vehicle detection under various lighting conditions by incorporating particle filter", in IEEE Proceedings of Intelligent Transportation System 2007, Seattle, WA, USA, pp. 534-539, Sept.30-Oct.3, 2007.
  2. S. Kim, S.Y. Oh, J. Kang, Y Ryu, K. Kim, s.c. Park, and K. Park, "Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2173-2178, Aug. 2005.
  3. Y.L. Chen, Y.H. Chen, C.J. Chen, and B.F. Wu, "Nighttime vehicle detection for driver assistance and autonomous vehicles", in Proc. IEEE 18th Intell. Con! on Pattern Recognition, Vol.I, pp. 687-690, Aug. 2006.
  4. Hyun-Koo Kim, Yeonghwan Ju, Jonghun Lee, Yongwan Park, Ho-Youl Jung, "Lane detection for adaptive control of autonomous vehicle", Candidate journal for Accreditation, Journal of IEMEK, Vol.4, No.4, pp. 180-188, 2009.
  5. Horn, Robot Vision, MIT Press, 1996, pp. 69-71.
  6. Rauch, H.E. Tung, F. Striebel, C.T., "Maximum likelihood estimates of linear dynamic systems", AIAA J Vol.3, No.8, pp. 1445-1450. 1965. https://doi.org/10.2514/3.3166
  7. David Meyer, Friedrich Leisch, and Kurt Hornik, "The support vector machine under test", Neurocomputing Vol.55, No.1-2, pp. 169-186, 2003. https://doi.org/10.1016/S0925-2312(03)00431-4
  8. Olson, David L. Delen, Dursun "Advanced data mining techniques", Springer; 1 edition (Feb 1, 2008), pp. 138, ISBN 3540769161.
  9. Raoul de Charette, Fawzi Nashashibi, "Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates", pp. 358-363., Intelligent Vehicles Symposium, 2009.
  10. Raoul de Charette, Fawzi Nashashibi, "Traffic light recognition using image processing compared to learning processes", pp. 333-338, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis USA, Oct.11-15, 2009.