Image Processing for Driver Assistance

  • Werner v. Seelen
  • Uwe Handmann
  • Thomas Kalinke
  • Christos Tzomakas
  • Martin Werner
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Systems for automated image analysis are useful for a variety of tasks and their importance is still growing due to technological advances and an increase of social acceptance. Especially in the field of driver assistance systems the progress in science has reached a level of high performance. Fully or partly autonomously guided vehicles, particularly for road-based traffic, pose high demands on the development of reliable algorithms due to the conditions imposed by natural environments. At the Institut für Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We introduce a system which extracts the important information from an image taken by a CCD camera installed at the rear view mirror in a car. The approach consists of a sequential and a parallel sensor and information processing. Three main tasks namely the initial segmentation (object detection), the object tracking and the object classification are realized by integration in the sequential branch and by fusion in the parallel branch. The main gain of this approach is given by the integrative coupling of different algorithms providing partly redundant information.

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References

  1. 1.
    S. Bohrer, T. Zielke und V. Freiburg. An Integrated Obstacle Detection Framework for Intelligent Cruise Control on Motorways. In Proceedings of the Intelligent Vehicles Symposium, Detroit, Seite 276–281, 1995.CrossRefGoogle Scholar
  2. 2.
    M.E. Brauckmann, C. Goerick, J. Groß und T. Zielke. Towards all around automatic visual obstacle sensing for cars. In Proceedings of the Intelligent Vehicles ’94 Symposium, Paris, Prance, Seite 79–84, 1994.CrossRefGoogle Scholar
  3. 3.
    A. Broggi. A Massively Parallel Approach to Real-Time Vision-Based Road Markings Detection. In Proceedings of the Intelligent Vehicles ‘95 Symposium, Detroit, USA, Seite 84–85, 1995.CrossRefGoogle Scholar
  4. 4.
    E.D. Dickmanns et al. The Seeing Passenger Car’VaMoRs-P’. In Proceedings of the Intelligent Vehicles 7 94 Symposium, Paris, France, Seite 68–73, 1994.Google Scholar
  5. 5.
    ELTEC Elektronik GmbH, Mainz. THINEDGE-Processor for Contour Matching. Hardware Manual, Rev. 1A, 1991.Google Scholar
  6. 6.
    M. Finke und K.-R. Müller. Estimating A-Posteriori Probabilities Using Stochastic Network Models. In Proceedings of the Summer School on Neural Networks, Bolder, Colorado, Seite 276–281, 1993.Google Scholar
  7. 7.
    C. Goerick. Local orientation coding and adaptive thresholding for real time early vision. Internal Report IRINI 94–05, Institut für Neuroinformatik, Ruhr- Universität Bochum, D-44780 Bochum, Germany, Juni 1994.Google Scholar
  8. 8.
    C. Goerick, D. Noll und M. Werner. Artificial Neural Networks in Real Time Car Detection and Tracking Applications. Pattern Recognition Letters, 1996.Google Scholar
  9. 9.
    U. Handmann und T. Kalinke. Fusion of texture and contour based methods for object recognition. In ITSC’97, IEEE Conference on Intelligent Transportation Systems 1997, Boston, 1997. IEEE. Session 35: Intelligent Vehicles: Vision(3).Google Scholar
  10. 10.
    U. Handmann, T. Kalinke, C. Tzomakas, M. Werner und W. v. Seelen. Computer vision for driver assistance systems. In Proceedings of SPIE Vol. 3364 Orlando, 1998. SPIE. Session Enhanced and Synthetic Vision 1998.Google Scholar
  11. 11.
    U. Handmann, G. Lorenz, T. Schnitger und W. v. Seelen. Fusion of different sensors and algorithms for segmentation. In IV’98, IEEE International Conference on Intelligent Vehicles 1998, Stuttgart, 1998. IEEE.Google Scholar
  12. 12.
    U. Handmann, G. Lorenz und W. von Seelen. Fusion von Basisalgorithmen zur Segmentierung von Strassenverkehrsszenen. In Mustererkennung 1998, Heidelberg, 1998. Springer-Verlag.Google Scholar
  13. 13.
    Robert M. Haralick, K Shanmugan und Its’hak Dinstein. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, 1973.Google Scholar
  14. 14.
    J.A. Hertz, A.S. Krogh und R.G. Palmer. Introduction to the Theory of Neural Computation. Addison Wesley, 1991.Google Scholar
  15. 15.
    K. Hornik, M. Stinchcombe und H. White. Multilayer Feedforward Networks are Universal Approximators. Neural Networks, 2: 359–366, 1989.CrossRefGoogle Scholar
  16. 16.
    D.P. Huttenlocher. Comparing Images Using the Hausdorff Distance. IEEE Transactions on PAMI, 15 (9), September 1993.Google Scholar
  17. 17.
    T. Kalinke und C. Tzomakas. Objekthypothesen in Verkehrsszenen unter Nutzung der Kamerageometrie. Internal Report IRINI 97–07, Institut für Neuroinformatik, Ruhr-Universität Bochum, D-44780 Bochum, Germany, 1997.Google Scholar
  18. 18.
    T. Kalinke und W. von Seelen. Entropie als Maß des lokalen Informationsgehalts in Bildern zur Realisierung einer Aufmersamkeitssteuerung. In Mustererkennung 1996, Seite 627–634, Heidelberg, 1996. Springer-Verlag.Google Scholar
  19. 19.
    T. Kalinke und W. von Seelen. Kullbach-Leibler Distanz als Maß zur Erkennung nicht rigider Objekte. In Mustererkennung 1997, Seite 501–508, Heidelberg, 1997. Springer-Verlag.Google Scholar
  20. 20.
    T. Kalinke und W. von Seelen. Kullback-Libler Distanz als Maß zur Erkennung nicht rigider Objekte. In Mustererkennung 1997, 1997.Google Scholar
  21. 21.
    H. Mori und N. M. Charkari. Shadow and Rhythm as Sign Patterns of Obstacle Detection. In International Symposium on Industrial Electronics, Seite 271–277, 1993.Google Scholar
  22. 22.
    D. Noll. Ein Optimierungsansatz zur Objekterkennung. Nummer 454 in Fortschrittberichte, Reihe 10. VDI-Verlag, Düsseldorf, 1996. Dissertation, Ruhr-Universität Bochum.Google Scholar
  23. 23.
    D.W. Paglieroni. Distance Transforms: Properties and Machine Vision Applications. CVGIP, 54 (l): 56–74, January 1991.CrossRefGoogle Scholar
  24. 24.
    D. Pomerleau. RALPH: Rapidly Adapting Lateral Position Handler. In Proceedings of the Intelligent Vehicles’95 Symposium, Detroit, USA, Seite 506–511, 1995.Google Scholar
  25. 25.
    T. Schnitger und U. Handmann. Fusion von Bildanalyseverfahren mittels einer neuronalen Kopplungsstruktur. Internal Report IRINI 98–01, Institut für Neuroinformatik, Ruhr-Universität Bochum, D-44780 Bochum, Germany, April 1998.Google Scholar
  26. 26.
    W. von Seelen et al. Image Processing of Dynamic Scenes. Internal Report IRINI 97–14, Institut für Neuroinformatik, Ruhr-Universität Bochum, D-44780 Bochum, Germany, Juli 1997.Google Scholar
  27. 27.
    M. Werner und W. v. Seelen. Using order statistics for object tracking. In Proceedings of the Intelligent Vehicles Symposium, Boston, Seite 323, Digest 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Werner v. Seelen
    • 1
  • Uwe Handmann
    • 1
  • Thomas Kalinke
    • 1
  • Christos Tzomakas
    • 1
  • Martin Werner
    • 1
  1. 1.Institut für NeuroinformatikRuhr Universität BochumBochumGermany

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