Multi-sensor People Counting

  • Daniel Hernández-Sosa
  • Modesto Castrillón-Santana
  • Javier Lorenzo-Navarro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

An accurate estimation of the number of people entering / leaving a controlled area is an interesting capability for automatic surveillance systems. Potential applications where this technology can be applied include those related to security, safety, energy saving or fraud control. In this paper we present a novel configuration of a multi-sensor system combining both visual and range data specially suited for troublesome scenarios such as public transportation. The approach applies probabilistic estimation filters on raw sensor data to create intermediate level hypothesis that are later fused using a certainty-based integration stage. Promising results have been obtained in several tests performed on a realistic test bed scenario under variable lightning conditions.

Keywords

people counting EKF MHI laser sensors 

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References

  1. 1.
    Bobick, A.F., Davis, J.W.: The recognition of human movem. IEEE Transactions on Intelligent Transportation Systems Transactions on Pattern Analysis and Machine Intelligence 23(257-267), 3 (2001)Google Scholar
  2. 2.
    Bozzoli, M., Cinque, L., Sangineto, E.: A statistical method for people counting in crowded environments. In: 14th International Conference on Image Analysis and Processing (2007)Google Scholar
  3. 3.
    Chan, A.B., Liang, Z.-S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: Counting people without people models or tracking. In: Computer Vision and Pattern Recognition, pp. 1–7 (2008)Google Scholar
  4. 4.
    Cui, J., Zha, H., Zhao, H., Shibasaki, R.: Multi-modal tracking of people using laser scanners and video camera. Image and Vision Computing 26(2), 240–252 (2008)CrossRefGoogle Scholar
  5. 5.
    Katabira, K., Nakamura, K., Zhao, H., Shibasaki, R.: A method for counting pedestrians using a laser range scanner. In: 25th Asian Conference on Remote Sensing (ACRS 2004), Thailand, November 22-26 (2004)Google Scholar
  6. 6.
    Kellokumpu, V., Zhao, G., Pietikinen, M.: Recognition of human actions using texture descriptors. Machine Vision and Applications (2010) (in press)Google Scholar
  7. 7.
    Lee, G.G., Ki Kim, H., Yoon, J.Y., Kim, J.J., Kim, W.Y.: Pedestrian counting using an IR line laser. In: International Conference on Convergence and Hybrid Information Technology 2008 (2008)Google Scholar
  8. 8.
    Mathews, E., Poigné, A.: Evaluation of a ”smart” pedestrian counting system based on echo state networks. EURASIP Journal on Embedded Systems, 1–9 (2009)Google Scholar
  9. 9.
    Scheutz, M., McRaven, J., Cserey, G.: Fast, reliable, adaptive, bimodal people tracking for indoor environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), vol. 2, pp. 1347–1352 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Hernández-Sosa
    • 1
  • Modesto Castrillón-Santana
    • 1
  • Javier Lorenzo-Navarro
    • 1
  1. 1.SIANIUniversidad de Las Palmas de Gran CanariaSpain

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