A Look at Feet: Recognizing Tailgating via Capacitive Sensing

  • Dirk Siegmund
  • Sudeep Dev
  • Biying Fu
  • Doreen Scheller
  • Andreas Braun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10922)


At many every day places, the ability to be reliably able to determine how many individuals are within an automated access control area, is of great importance. Especially in high-security areas such as banks and at country borders, access systems like mantraps or drop-arm turnstiles serve this purpose. These automated systems are designed to ensure that only one person can pass through a particular transit area at a time. State of the art systems use camera systems mounted in the ceiling to detect people sneaking in behind authorized individuals to pass through the transit space (tailgating attacks). Our novel method is inspired by recently achieved results in capacitive in-door-localization. Instead of estimating the position of humans, the pervasive capacitance of feet in the transit space is measured to detect tailgating attacks. We explore suitable sensing techniques and sensor-grid layout to be used for that application. In contrast to existing work, we use machine learning techniques for classification of the sensor’s feature vector. The performance is evaluated on hardware-level, by defining its physical effectiveness. Tests with simulated attacks show its performance in comparison with competitive camera-image methods. Our method provides verification of tailgating attacks with an equal-error-rate of 3.5%, which outperforms other methods. We conclude with an evaluation of the amount of data needed for classification and highlight the usefulness of this method when combined with other imaging techniques.


Capacitive sensing Human machine interface People counting Mantrap portal Tailgating 


  1. 1.
    McGovern, M.: Upgrading your entrance lanes (2013)Google Scholar
  2. 2.
    Edam, B.: Mantrap portal solution, July 2016Google Scholar
  3. 3.
    Amrehn & Partner EDV-Service GmbH: Bioporta, July 2016Google Scholar
  4. 4.
    Siegmund, D., Handtke, D., Kaehm, O.: Verifying isolation in a mantrap portal via thermal imaging. In: 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–4, May 2016Google Scholar
  5. 5.
    Siegmund, D., Wainakh, A., Braun, A.: Verification of single-person access in a mantrap portal using RGB-D images. In: XII Workshop de Visao Computacional (WVC), November 2016Google Scholar
  6. 6.
    Siegmund, D., Fu, B., Samartzidis, T., Wainakh, A., Kuijper, A., Braun, A.: Attack detection in an autonomous entrance system using optical flow. In: 7th International Conference on Crime Detection and Prevention (ICDP 2016), pp. 1–6. IET (2016)Google Scholar
  7. 7.
    Feng, G., Yang, Y., Guo, X., Wang, G.: A smart fiber floor for indoor target localization. IEEE Pervasive Comput. 14(2), 52–59 (2015)CrossRefGoogle Scholar
  8. 8.
    Steinhage, A., Lauterbach, C.: Monitoring movement behavior by means of a large area proximity sensor array in the floor. In: BMI, pp. 15–27 (2008)Google Scholar
  9. 9.
    Braun, A., Heggen, H., Wichert, R.: CapFloor – a flexible capacitive indoor localization system. In: Chessa, S., Knauth, S. (eds.) EvAAL 2011. CCIS, vol. 309, pp. 26–35. Springer, Heidelberg (2012). Scholar
  10. 10.
    Braun, A., Wichert, R., Kuijper, A., Fellner, D.W.: Capacitive proximity sensing in smart environments. J. Ambient Intell. Smart Environ. 7(4), 483–510 (2015)CrossRefGoogle Scholar
  11. 11.
    Valtonen, M., Maentausta, J., Vanhala, J.: Tiletrack: capacitive human tracking using floor tiles. In: IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, pp. 1–10. IEEE (2009)Google Scholar
  12. 12.
    Baxter, L.K.: Capacitive Sensors: Design and Applications. Wiley, New York (1996)CrossRefGoogle Scholar
  13. 13.
    Smith, J.R.: Field mice: extracting hand geometry from electric field measurements (1996)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric Adaboost and a detector cascade. In: NIPS, Vancouver, British Columbia, Canada, vol. 2001, pp. 1311–1318 (2001)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dirk Siegmund
    • 1
  • Sudeep Dev
    • 1
  • Biying Fu
    • 1
  • Doreen Scheller
    • 1
  • Andreas Braun
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany

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