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Journal of Bionic Engineering

, Volume 5, Issue 3, pp 247–252 | Cite as

RETRACTED ARTICLE: Fast and Robust Stereo Vision Algorithm for Obstacle Detection

  • Yi-peng ZhouEmail author
Article

Abstract

Binocular computer vision is based on bionics, after the calibration through the camera head by double-exposure image synchronization, access to the calculation of two-dimensional image pixels of the three-dimensional depth information. In this paper, a fast and robust stereo vision algorithm is described to perform in-vehicle obstacles detection and characterization. The stereo algorithm which provides a suitable representation of the geometric content of the road scene is described, and an in-vehicle embedded system is presented. We present the way in which the algorithm is used, and then report experiments on real situations which show that our solution is accurate, reliable and efficient. In particular, both processes are fast, generic, robust to noise and bad conditions, and work even with partial occlusion.

Keywords

stereo vision vehicle dynamics visibility range image alignment 

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References

  1. [1]
    Hattori H, Maki A. Stereo without depth search and metric calibration. Conference on Computer Vision and Pattern Recognition, Kawasaki, Japan, 2000, 177–184.Google Scholar
  2. [2]
    Hautiere N, Labayrade R, Aubert D. Real-time disparity contrast combination for onboard estimation of the visibility distance. IEEE Transactions on Intelligent Transportation Systems, 2006, 7, 201–212.CrossRefGoogle Scholar
  3. [3]
    Labayrade R, Aubert D, Tarel J P. Real time obstacle detection on non flat road geometry through v-disparity representation. Proceedings of IEEE Intelligent Vehicles Symposium, Versailles, France, 2002, 2, 646–651.Google Scholar
  4. [4]
    Labayrade R, Aubert D. A single framework for vehicle roll,pitch, yaw estimation and obstacles detection by stereovision. Proceedings of IEEE Intelligent Vehicles Symposium, Columbus, USA, 2003, 31–36.Google Scholar
  5. [5]
    Gruyer D, Berge-Cherfaoui V. Multi-objects association in perception of dynamical situation. Proceedings of the 15th Conference in Uncertainty in Artificial Intelligence, Stockholm, Sweden, 1999, 255–262.Google Scholar
  6. [6]
    Labayrade R, Aubert D. In-Vehicle obstacle detection and characterization by stereovision. The 1st International Workshop on In-Vehicle Cognitive Computer Vision Systems, Graz, Austria, 2003, 13–19.Google Scholar
  7. [7]
    Williamson T A. A High-Performance Stereo Vision System for Obstacle Detection. PhD Thesis, Carnegie Mellon University, USA, 1998.Google Scholar
  8. [8]
    Franke U, Joos A. Real-time stereo vision for urban traffic scene understanding. Proceedings of the IEEE Intelligent Vehicles Symposium, Dearborn, USA, 2000, 273–278.Google Scholar
  9. [9]
    Goldbeck J, Huertgen B. Lane detection and tracking by video sensors. Proceedings of International Conference on Intelligent Transportation Systems, Tokyo, 1999, 74–79.Google Scholar
  10. [10]
    Hancock J. High-Speed Obstacle Detection for Automated Highway Applications. Technical Report CMU-RI-TR-97-17, Robotics Institute, Carnegie Mellon University, 2000.Google Scholar
  11. [11]
    Rojas J C, Crisman J D. Vehicle detection in color images. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Boston, USA, 1997, 403–408.CrossRefGoogle Scholar
  12. [12]
    Grimson W E L. Computational experiments with a feature based stereo algorithm. IEEE Transactions on Pattern and Machine Intelligence, 1985, 7, 17–34.CrossRefGoogle Scholar
  13. [13]
    Kalinke T, Tzomakas C, Seelen W V. A Texture-based object detection and an adaptive model-based classification. Proceedings of the IEEE Intelligent Vehicles Symposium, Stuttgart, Germany, 1998, 1, 143–148.Google Scholar
  14. [14]
    Bertozzi M, Broggi A, Fascioli A, Nichele S. Stereo vision-based vehicle detection. Proceedings of the IEEE Intelligent Vehicles Symposium, Dearborn, USA, 2000, 39–44.Google Scholar

Copyright information

© Jilin University 2008

Authors and Affiliations

  1. 1.Department of AutomationNorthwestern Polytechnic UniversityXianP. R. China

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