Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Preceding Vehicles Based on a Multistage Combination of Edge Features and Horizontal Symmetry

에지특징의 단계적 조합과 수평대칭성에 기반한 선행차량검출

  • 송광열 (전남대학교 산업공학과(자동차연구소)) ;
  • 이준웅 (전남대학교 산업공학과(자동차연구소))
  • Published : 2008.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents an algorithm capable of detecting leading vehicles using a forward-looking camera. In fact, the accurate measurements of the contact locations of vehicles with road surface are prerequisites for the intelligent vehicle technologies based on a monocular vision. Relying on multistage processing of relevant edge features to the hypothesis generation of a vehicle, the proposed algorithm creates candidate positions being the left and right boundaries of vehicles, and searches for pairs to be vehicle boundaries from the potential positions by evaluating horizontal symmetry. The proposed algorithm is proven to be successful by experiments performed on images acquired by a moving vehicle.

Keywords

References

  1. S. M. Smith and J. M. Brady, ASSET-2: Real time motion segmentation and shape tracking, IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 8, pp. 814-820, 1995 https://doi.org/10.1109/34.400573
  2. M. Xie, L. Trassoudaine, J. Alizon, and J. Gallice, Road Obstacle Detection and Tracking by an Active and Intelligent Sensing Strategy, Machine Vision and Applications, vol. 7, pp. 165-177 https://doi.org/10.1007/BF01211661
  3. T. Zielke, M. Brauckmann and W. V. Seelen, Intensity and Edge-Based Symmetry Detection with an Application to Car-Following, CVGIP:Image Understanding, vol. 58, no. 2, pp. 177-190, 1993 https://doi.org/10.1006/ciun.1993.1037
  4. R. Y. Tsai, "A versatile camera calibration technique for highaccuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses," IEEE Robotics and Automation, vol. RA-3, no. 4, pp. 323-344, 1987
  5. R. G. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, 1992
  6. A. Broggi, P. Cerri, and P. C. Antonello, Multi-Resolution Vehicle Detection using Artificial Vision, IV '04, pp. 310-314, 2004
  7. J. W. Lee and I. S. Kweon, "Map-based probabilistic reasoning to vehicle segmentation," Pattern Recognition, vol. 31, no. 12, pp. 2017-2026, 1998 https://doi.org/10.1016/S0031-3203(98)00027-2
  8. J. Y. Chang and C. W. Cho, "Vision-based forward-looking traffic scene analysis scheme," IV '07, pp. 56-61, 2007
  9. Z. Sun, G. Bebis and R. Miller, "On-road vehicle detection using evolutionary gabor filter optimization," IEEE Trans. Intell. Transport Syst., vol. 6, no. 2, pp.125-137, 2005 https://doi.org/10.1109/TITS.2005.848363
  10. J. Ferryman, A. Worral., G. Sullivan, and K. Baker, "A generic deformable model for vehicle recogntion," Proceedings of British Machine Vision Conference, pp. 127-136, 1995
  11. T. Kalinke, C. Tzomakas, and W. V. Seelen, "A texture-based object detection and an adaptive model-based classification," IV '98, pp. 143-148, 1998
  12. M. B. V. Leeuwen and F. C. A. Groen, "Vehicle detection with a mobile camera-supporting midrange, distant, and passing cars," IEEE Robotics & Automation Magazine March, pp. 37-42, 2005
  13. A. Khammari, F. Nashashibi, Y. Abramson, and C. Laurgeau, "Vehicle detection combining gradient analysis and AdaBoost classification," IV '05, pp. 1084-1089, 2005
  14. J. W. Lee, U. K. Yi, and K. R. Baek, "A cumulative distribution function of edge direction for road lane detection," IEICE E84-D, vol. 9, pp. 1206-1216, 2001
  15. O. Faugeras, "Three-dimensional computer vision-a geometric viewpoint," The MIT Press, 1993
  16. J. W. Lee and U. K. Yi, "A lane-departure identification based on LBPE, hough transform, and linear regression," CVIU, vol. 99, pp. 359-383, 2005 https://doi.org/10.1016/j.cviu.2005.03.002