Recognition of a Close Leading Vehicle Using the Contour of the Vehicles Wheels

차량 뒷바퀴 윤곽선을 이용한 근거리 전방차량인식

  • Published : 2001.03.01

Abstract

This paper describes a method for detecting a close leading vehicle using the contour of the vehi-cles rear wheels. The contour of a leading vehicles rear wheels in 속 front road image from a B/W CCD camera mounted on the central front bumper of the vehicle, has vertical components and can be discerned clearly in contrast to the road surface. After extracting positive edges and negative edges using the Sobel op-erator in the raw image, every point that can be recognized as a feature of the contour of the leading vehicle wheel is determined. This process can detect the presence of a close leading vehicle, and it is also possible to calculate the distance to the leading vehicle and the lateral deviation angle. This method might be useful for developing and LSA (Low Speed Automation) system that can relieve drivers stress in the stop-and-go traffic conditions encoun-tered on urban roads.

Keywords

References

  1. H. Satonaka, Y. Hashmoto, Y. Yamada, and T. Kakiname, 'A study of sensor fusion technology for collision avoidance system,' Proc. of Intelligent Transport Systems, pp. 1108-1115, 1995
  2. U. Solder and V. Graefe, 'Object detection in real time,' SPIE Symposium on Advances in Intelligent Systems, vol. 1388, pp. 112-119, 1990
  3. T. Zielke, M. Brauckmann, and W. Seelen, 'Intensity and Edge-Base symmetry detection with an application to Car-Following,' CVGIP : Image Understanding 58, pp. 177-190, 1993
  4. A. Kuehnle, 'Symmetry-Based recognition of vehicle rears,' Pattern Recognition Letters 12, pp. 249-258, 1991 https://doi.org/10.1016/0167-8655(91)90039-O
  5. J. C. Burie and J. G. Posraire, 'Enhancement of the road safety with a stereovision system based on linear cameras,' Proc. of Intelligent Vehicles 96, pp. 147-152, 1994 https://doi.org/10.1109/IVS.1996.566369
  6. Bertozzi, Massimo, Broggi, and Alberto, 'Real-Time lane and obstacle detection on the GOLD system,' Proc. of Intelligent Vehicles 96, pp. 213-217, 1996 https://doi.org/10.1109/IVS.1996.566380
  7. U. Franke and I. Kutzbach, 'Fast stereo based object detection for Stop&Go traffic,' Proc. of IEEE Intelligent Vehicles 96, pp. 339-344, 1996 https://doi.org/10.1109/IVS.1996.566403
  8. A. Giachetti, M. Campani, R. Sanni, A. Succi, 'The recovery of optical flow for intelligent cruise control,' Proc. of Intelligent Vehicles 95, pp. 91-96, 1994 https://doi.org/10.1109/IVS.1994.639479
  9. W. Enkelmann, 'Obstacle detection by evaluation of optical flow fields from image se quences,' Proc. of First European Conference on Computer Vision, pp. 134-138, 1990 https://doi.org/10.1007/BFb0014859
  10. Betke, Maegrit, Haritaoglu, Esin, Davis, and Larry, 'Multiple vehicle detection in hard Real-Time,' Proc. of Intelligent Vehicles, pp. 351-356, 1996 https://doi.org/10.1109/IVS.1996.566405
  11. C. C. Lai and W. H. Tsai, 'Estimation of moving vehicle locations using wheel shape information in single 2-D lateral vehicle images by 3-D computer vision techniques,' Robotics and Computer-Integrated Manufacturing, vol. 15, no. 2, pp. 111-120, 1999 https://doi.org/10.1016/S0736-5845(99)00006-X
  12. E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, 1996