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Machine Vision and Applications

, Volume 16, Issue 2, pp 85–95 | Cite as

Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera

  • Tarak GandhiEmail author
  • Mohan Trivedi
Article

Abstract.

Omnidirectional cameras that give a 360° panoramic view of the surroundings have recently been used in many applications such as robotics, navigation, and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile-mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omnidirectional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from a CAN bus are integrated with the motion information from spatial and temporal gradients using a Bayesian approach. The approach is tested for various configurations of an automobile-mounted omni camera as well as a rectilinear camera. Successful detection and tracking of moving vehicles and generation of a surround map are demonstrated for application to intelligent driver support.

Keywords:

Motion estimation Panoramic vision Intelligent vehicles Driver support systems Collision avoidance 

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References

  1. 1.
    Achler O, Trivedi MM (2002) Real-time traffic flow analysis using omnidirectional video network and flatplane transformation. In: Workshop on intelligent transportation systems. ChicagoGoogle Scholar
  2. 2.
    Achler O, Trivedi MM (2004) Vehicle wheel detector using 2d filter banks. In: Proc. IEEE symposium on intelligent vehicles, pp 25-30Google Scholar
  3. 3.
    Adiv G (1985) Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Trans Pattern Anal Mach Intell 7(4):384-401Google Scholar
  4. 4.
    Bar-Shalom Y, Li XR, Kirubarajan T (2001) Estimation with applications to tracking and navigation. Wiley, New YorkGoogle Scholar
  5. 5.
    Benosman R, Kang SB (2001) Panoramic vision: sensors, theory, and applications. Springer, Berlin Heidelberg New YorkGoogle Scholar
  6. 6.
    Bertozzi M, Broggi A (1998) Gold: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans Image Proc 7(1):62-81Google Scholar
  7. 7.
    Black MJ, Anandan P (1996) The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Comput Vis Image Understand 63(1):75-104Google Scholar
  8. 8.
    Daniilidis K, Makadia A, Bulow T (2002) Image processing in catadioptric planes: spatiotemporal derivatives and optical flow computation. In: IEEE workshop on omnidirectional vision, pp 3-12Google Scholar
  9. 9.
    Danuser G, Stricker M (1998) Parametric model fitting: from inlier characterization to outlier detection. IEEE Trans Pattern Anal Mach Intell 20(2):263-280Google Scholar
  10. 10.
    Enkelmann W (2001) Video-based driver assistance: from basic functions to applications. Int J Comput Vis 45(3):201-221Google Scholar
  11. 11.
    Faugeras O (1993) Three-dimensional computer vision: a geometric viewpoint. MIT Press, Cambridge, MAGoogle Scholar
  12. 12.
    Forsyth D, Ponce J (2003) Computer vision: a modern approach. Prentice-Hall, Upper Saddle River, NJGoogle Scholar
  13. 13.
    Gandhi T, Trivedi MM (2003) Motion analysis of omni-directional video streams for a mobile sentry. In: 1st ACM international workshop on video surveillance, pp 49-58, Berkeley, CAGoogle Scholar
  14. 14.
    Gandhi T, Trivedi MM (2004) Motion based vehicle surround analysis using omni-directional camera. In: Proc. IEEE symposium intelligent vehicles, pp 560-565Google Scholar
  15. 15.
    Gluckman J, Nayar S (1998) Ego-motion and omnidirectional cameras. In: Proc. international conference on computer vision, pp 999-1005Google Scholar
  16. 16.
    Hicks RA, Bajcsy R (1999) Reflective surfaces as computational sensors. In: Proc. 2nd workshop on perception for mobile agents, pp 82-86Google Scholar
  17. 17.
    Horn B, Schunck B (1981) Determining optical flow. In: DARPA81, pp 144-156Google Scholar
  18. 18.
    Huang, K, Trivedi MM, Gandhi T (2003) Driver’s view and vehicle surround estimation using omnidirectional video stream. In: IEEE symposium intelligent vehicles, Columbus, OH, pp 444-449Google Scholar
  19. 19.
    Huang KC, Trivedi MM (2003) Video arrays for real-time tracking of persons, head and face in an intelligent room. Mach Vis Appl 14(2):103-111Google Scholar
  20. 20.
    Irani M, Anandan P (1998) A unified approach to moving object detection in 2D and 3D scenes. IEEE Trans Pattern Anal Mach Intell 20(6):577-589Google Scholar
  21. 21.
    Irani M, Rousso B, Peleg S (1994) Computing occluding and transparent motions. Int J Comput Vis 12:5-16Google Scholar
  22. 22.
    Jähne B, Haußecker H, Geißler P (1999) Handbook of Computer Vision and Applications, vol 2, chap 14, pp 397-422. Academic Press, San Diego, CAGoogle Scholar
  23. 23.
    Kruger W (1999) Robust real time ground plane motion compensation from a moving vehicle. Mach Vis Appl 11:203-212Google Scholar
  24. 24.
    Labayrade R, Aubert D, Tarel J-P (2002) Real time obstacle detection in stereovision on non flat road geometry through v-disparity representation. In: IEEE symposium intelligent vehicles, 2:646-651Google Scholar
  25. 25.
    Lourakis MIA, Orphanoudakis SC (1998) Visual detection of obstacles assuming a locally planar ground. In: Asian conference on computer vision, 2:527-534Google Scholar
  26. 26.
    Shakernia O, Vidal R, Sastry S (2003) Omnidirectional egomotion estimation from back-projection flow. In: IEEE workshop on omnidirectional visionGoogle Scholar
  27. 27.
    Simoncelli EP (1993) Coarse-to-fine estimation of visual motion. In: Proc. 8th workshop on image and multidimensional signal processing, Cannes, France, pp 128-129Google Scholar
  28. 28.
    Svoboda T, Pajdla T, Hlaváč V (1998) Motion estimation using central panoramic cameras. In: IEEE international conference on intelligent vehicles, pp 335-340Google Scholar
  29. 29.
    Trucco E, Verri A (1998) Computer vision and applications: a guide for students and practitioners. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  30. 30.
    Vassallo RF, Santos-Victor J, Schneebeli HJ (2002) A general approach for egomotion estimation with omnidirectional images. In: IEEE workshop on omnidirectional vision, pp 97-103Google Scholar

Copyright information

© Springer-Verlag Berlin/Heidelberg 2005

Authors and Affiliations

  1. 1.Computer Vision and Robotics Research LaboratoryUniversity of California at San DiegoLa JollaUSA

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