Intelligent Overhead Sensor for Sliding Doors: A Stereo Based Method for Augmented Efficiency

  • Luca Bombini
  • Alberto Broggi
  • Michele Buzzoni
  • Paolo Medici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

This paper describes a method to detect and extract pedestrians trajectories in proximity of a sliding door access in order to automatically open the doors: if a pedestrian walks towards the door, the system opens the door. On the other hand if the pedestrian trajectory is parallel to the door, the system does not open. The sensor is able to self-adjust according to changes in weather conditions and environment. The robustness of this system is provided by a new method for disparity image extraction.

The rationale behind this work is that the device developed in this paper avoids unwanted openings in order to decrease needs for maintenance, and increase building efficiency in terms of temperature (i.e. heating and air conditioning). The algorithm has been tested in real conditions to measure its capabilities and estimate its performance.

Keywords

safety sensor sliding doors obstacle detection pedestrian detection trajectory planning stereo vision 

References

  1. 1.
    Bertozzi, M., Broggi, A., Fascioli, A.: Stereo Inverse Perspective Mapping:Theory and Applications. Image and Vision Computing Journal 8(16), 585–590 (1998)CrossRefGoogle Scholar
  2. 2.
    Bertozzi, M., Broggi, A., Fascioli, A.: An extension to the Inverse Perspective Mapping to handle non-flat roads. In: Procs. IEEE Intelligent Vehicles Symposium 1998, Stuttgart, Germany, pp. 305–310 (1998)Google Scholar
  3. 3.
    Felisa, M., Zani, P.: Incremental Disparity Space Image computation for automotive applications. In: Procs. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, St.Louis, Missouri, USA (October 2009)Google Scholar
  4. 4.
    Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithms. In: ICPR 2008 19th International Conference on Pattern Recognition (December 2008)Google Scholar
  5. 5.
    Claus, D., Fitzgibbon, A.W.: A rational function lens distortion model for general cameras. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, Calif., USA, vol. 1, pp. 213–219 (2005)Google Scholar
  6. 6.
    Devernay, F., Faugeras, O.: Straight lines have to be straight. Machine Vision and Applications 13(1), 14–24 (2001)CrossRefGoogle Scholar
  7. 7.
    Tsai, R.Y.: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation, 323–334 (1987)Google Scholar
  8. 8.
    Jahne, B.: Digital Image Processing, 5th edn. Springer, Berlin (2002)CrossRefMATHGoogle Scholar
  9. 9.
    Pratt, W.K.: Digital Image Processing, 3rd edn. Addison-Wesley, Milano (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Bombini, L., Buzzoni, M., Felisa, M., Medici, P.: Sistema per il Controllo di Porte Automatiche (March 2010), CCIAA di Milano, Patent application MI2010A000460Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luca Bombini
    • 1
  • Alberto Broggi
    • 1
  • Michele Buzzoni
    • 1
  • Paolo Medici
    • 1
  1. 1.VisLab – Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di ParmaItaly

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