Advertisement

A Real Time Human Detection System Based on Far Infrared Vision

  • Yannick Benezeth
  • Bruno Emile
  • Hélène Laurent
  • Christophe Rosenberger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

We present in this article a human detection and tracking algorithm using infrared vision in order to have reliable information on a room occupation. We intend to use this information to limit energetic consumption (light, heating). We perform first, a foreground segmentation with a Gaussian background model. A tracking step based on connected components intersections allows to collect information on 2D displacements of moving objects in the image plane. A classification based on a cascade of boosted classifiers is used for the recognition. Experimental results show the efficiency of the proposed algorithm.

References

  1. 1.
    Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.: Automated Detection of Human for Visual Surveillance System. In: Proceedings of the International Conference on Pattern Recognition, pp. 865–869 (1996)Google Scholar
  2. 2.
    Dedeoglu, Y.: Moving object detection, tracking and classification for smart video surveillance, PhD thesis, bilkent university (2004)Google Scholar
  3. 3.
    Mae, Y., Sasao, N., Inoue, K., Arai, T.: Person detection by mobile-manipulator for monitoring. In: The Society of Instrument and Control Engineers Annual Conference (2003)Google Scholar
  4. 4.
    Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: 6th International Conference on Computer Vision, pp. 555–562 (1998)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  7. 7.
    Yoon, S., Kim, H.: Real-time multiple people detection using skin color, motion and appearance information. In: Proc. IEEE International Workshop on Robot and Human Interactive Communication, pp. 331–334 (2004)Google Scholar
  8. 8.
    Haritaoglu, I., Harwood, D., David, L.S.: W4: real-time surveillance of people and their activities. IEEE Transaction on Pattern Analysis and Machine Intelligence, 809–830 (2006)Google Scholar
  9. 9.
    Stauffer, C., Grimson, E.: Adaptive background mixture models for real-time tracking, CVPR, 246–252 (1999)Google Scholar
  10. 10.
    Benezeth, Y., Emile, B., Rosenberger, C.: Comparative Study on Foreground Detection Algorithms for Human Detection. In: Proceedings of the Fourth International Conference on Image and Graphics, pp. 661–666 (2007)Google Scholar
  11. 11.
    Han, J., Bhanu, B.: Detecting moving humans using color and infrared video. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 228–233 (2003)Google Scholar
  12. 12.
    Davis, J., Keck, M.: A two-stage approach to person detection in thermal imagery. In: Proc. Workshop on Applications of Computer Vision (2005)Google Scholar
  13. 13.
    Davis, J., Sharma, V.: Background-Subtraction using Contour-based Fusion of Thermal and Visible Imagery. Computer Vision and Image Understanding, 162–182 (2007)Google Scholar
  14. 14.
    Schapire, R.E.: The boosting approach to machine learning: An overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yannick Benezeth
    • 1
  • Bruno Emile
    • 1
  • Hélène Laurent
    • 1
  • Christophe Rosenberger
    • 2
  1. 1.Institut Prisme, ENSI de BourgesUniversité d’OrléansBourges cedexFrance
  2. 2.Laboratoire GREYC, ENSICAENUniversité de Caen CNRSCaenFrance

Personalised recommendations