Data Simulation and Testing of Visual Algorithms in Synthetic Environments for Security Sensor Networks

  • Georg Hummel
  • Levente Kovács
  • Peter Stütz
  • Tamás Szirányi
Part of the Communications in Computer and Information Science book series (CCIS, volume 318)


Current development of security sensor networks and their processing algorithms use pre-recorded or abstract data streams for testing, often missing important ground truth for validation. This paper proposes a simulation-based test bed, presenting an approach to use commercial off the shelf virtual reality environments to create adaptive simulations of sensors, data streams and scenarios for training and testing perceptive algorithms. An example using airborne visual multi object tracking is discussed and validated.


virtual reality simulation security networks computer vision 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Medusa Consortium: Multi Sensor Data Fusion Grid for Urban Situational Awareness,
  2. 2.
    Collins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for cooperative multisensor surveillance. Proc. of the IEEE 89(10), 1456–1477 (2001)CrossRefGoogle Scholar
  3. 3.
    Doulaverakis, C., Konstantinou, N., Knape, T., Kompatsiaris, I., Soldatos, J.: An approach to intelligent information fusion in sensor saturated urban environments. In: Proc. of IEEE European Intelligence and Security Informatics Conference (EISIC), pp. 108–115 (2011)Google Scholar
  4. 4.
    Burger, W., Barth, M.J.: Virtual Reality for enhanced computer vision. In: Proc. IFIP 5.10 Workshop on Virtual Environments (1994)Google Scholar
  5. 5.
    Hummel, G., Stütz, P.: Conceptual design of a simulation test bed for ad-hoc sensor networks based on a serious gaming environment. In: Proc. Intl. Training and Education Conference, ITEC (2011)Google Scholar
  6. 6.
    Kovács, L.: Benedek, C.: Visual real-time detection, recognition and tracking of ground and airborne targets. In: Proc. Computational Imaging IX, SPIE-IS&T Electronic Imaging, vol. 7873, pp. 787311-1–787311-12. SPIE (2011)Google Scholar
  7. 7.
    Máttyus, G., Benedek, C., Szirányi, T.: Multi target tracking on aerial videos. In: Proc. of ISPRS Workshop on Modeling of Optical Airborne and Space Borne Sensors (2010)Google Scholar
  8. 8.
    Taixe, L.L., Heydt, M., Rosenhahn, A., Rosenhahn, B.: Automatic tracking of swimming microorganisms in 4D digital in-line holography data. In: Proc. of WMVC, pp. 1–9 (2009)Google Scholar
  9. 9.
    Virtual Battlespace 2, Bohemia Interactive Simulations,
  10. 10.
    Amir, Y., Nita-Rotaru, C., Stanton, J., Tsudik, G.: Secure Spread: An Integrated Architecture for Secure Group Communication. IEEE Trans. on DSC 2(3), 248–266 (2005)Google Scholar
  11. 11.
    Salehi, A., Riahi, M., Michel, S., Aberer, K.: GSN, middleware for Stream World. In: Proc. of 10th Intl. Conf. on Mobile Data Management, MDM (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Georg Hummel
    • 1
  • Levente Kovács
    • 2
  • Peter Stütz
    • 1
  • Tamás Szirányi
    • 2
  1. 1.Institute of Flight SystemsUniversität der Bundeswehr MünchenNeubibergGermany
  2. 2.Computer and Automation Research InstituteHungarian Academy of SciencesHungary

Personalised recommendations