A Security Assistance System Combining Person Tracking with Chemical Attributes and Video Event Analysis

  • Christopher Becher
  • G.L. Foresti
  • Peter Kaul
  • W. Koch
  • F.P. Lorenz
  • D. Lubczyk
  • C. Micheloni
  • C. Piciarelli
  • K. Safenreiter
  • C. Siering
  • M. Varela
  • S.R. Waldvogel
  • M. Wieneke
Chapter

Abstract

Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.

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References

  1. [1]
    J. Janata (Ed.), Special Issue on Modern Topics in Chemical Sensing, Chem. Rev., vol. 108, pp. 327-844, 2008.Google Scholar
  2. [2]
    B. Neubig, and W. Brise, Das große Quarzkochbuch, Franzis-Verlag, Feldkirchen, 1997.Google Scholar
  3. [3]
    G. Sauerbrey, The use of quartz oscillators for weighing thin layers and for microweighing, Z. Phys. vol. 155, pp. 206-222, 1959.CrossRefGoogle Scholar
  4. [4]
    J. Bargon, B. Graewe, T. Jonischkeit, and K. Woelk, Sensorik Von der Waage zur elektronischen Nase, Chem. Unserer Zeit, vol. 37, pp. 212-213, 2003.CrossRefGoogle Scholar
  5. [5]
    J.C. Oxley, J.L. Smith, K. Shinde, J. Moran, Determination of the vapor density of triacetone triperoxide (TATP) using a gas chromatography headspace technique, Propellants, Explosives, Pyrotechnics, vol. 30, pp. 127-130, 2005.CrossRefGoogle Scholar
  6. [6]
    C. Heil, G.R. Windscheif, S. Braschohs, J. Floerke, J. Glaeser, M. Lopez, J. Mueller-Albrecht, U. Schramm, J. Bargon, and F. Voegtle, Highly selective sensor materials for discriminating carbonyl compounds in the gas phase using quartz microbalances, Sens. Actuators B, vol. 61, pp. 51-58, 1999.CrossRefGoogle Scholar
  7. [7]
    S.R. Waldvogel, J.W. Lörgen, D. Lubczyk, K. Müllen, and R. Bauer, Novel Device for the on-line Detection of TATP, DE1020080086606, 2008.Google Scholar
  8. [8]
    Y. Ivanov and A. Bobick, “Recognition of visual activities and interactions by stochastic parsing,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 852–872, 2000.CrossRefGoogle Scholar
  9. [9]
    D. Minnen, I. Essa, and T. Starner, “Expectation grammars: leveraging high-level expectations for activity recognition,” in Computer Vision and Pattern Recognition, 2003, pp. II–626–632.Google Scholar
  10. [10]
    D. Moore and I. Essa, “Recognizing multitasked activities using stochastic context-free grammar,” in IEEE Int. Conf. Con Computer Vision and Pattern Recognition, Kauai, HI, USA, 2001.Google Scholar
  11. [11]
    V. Vu, F. Brémond, and M. Thonnat, “Automatic video interpretation: A novel algorithm for temporal scenario recognition,” in 8th International Joint Conference on Artificial Intelligence, Acapulco, MEX, 2003, pp. 9–15.Google Scholar
  12. [12]
    S. Hongeng, R. Nevatia, and F. Brémond, “Video based even recognition: activity representation and probabilistic methods,” Computer Vision and Image Understanding, vol. 96, pp. 129–162, 2004.CrossRefGoogle Scholar
  13. [13]
    N. Moënne-Loccoz, F. Brémond, and M. Thonnat, “Recurrent bayesian network for the recognition of human behaviors from video,” in Int. Conf. On Computer Vision Systems, Graz, Austria, 2003, pp. 68–78.Google Scholar
  14. [14]
    H. Zhong, J. Shi, and M. Visontai, “Detecting unusual activity in video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, Washington DC, U.S.A, 27 June – 2 July 2004, pp. 819–826.Google Scholar
  15. [15]
    G.L. Foresti, C. Micheloni, L. Snidaro, P. Remagnino, and T. Ellis, “Active videobased surveillance systems,” IEEE Signal Processing Magazine, vol. 22, no. 2, pp. 25–37, March 2005.CrossRefGoogle Scholar
  16. [16]
    D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 25, no. 5, pp. 564–575, May 2003.CrossRefGoogle Scholar
  17. [17]
    G. Foresti and T. Dolso, “Adaptive high-order neural trees for pattern recognition,” IEEE Transactions on System, Man and Cybernetics Part B, vol. 34, no. 2, pp. 988–996, Apr. 2004.CrossRefGoogle Scholar
  18. [18]
    C. Micheloni, L. Snidaro, and G.L. Foresti, “Statistical event analysis for video surveillance,” in Proceedings of the Sixth IEEE International Workshop on Video Surveillance, Graz, Austria, May 13th 2006, pp. 81–88.Google Scholar
  19. [19]
    D. Conte, P. Foggia, C. Sansone, and M. Vento, “Thirty years of graph matching in pattern recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 3, pp. 265–298, 2004.CrossRefGoogle Scholar
  20. [20]
    Y. Bar-Shalom and T.E. Fortmann, Tracking and Data Association, Academic Press, 1988.Google Scholar
  21. [21]
    Y. Bar-Shalom, X.R. Li and T. Kirubarajan, Estimation with Applications to Tracking and Navigation, John Wiley & Sons, Inc., 2001.Google Scholar
  22. [22]
    S.S. Blackman and R. Populi, Design and Analysis of Modern Tracking Systems, Artech House, Boston (USA), 1999.Google Scholar
  23. [23]
    S.S. Blackman, Multiple-Target Tracking with Radar Applications, Artech House, Boston (USA), 1986.Google Scholar
  24. [24]
    S.J. Davey, Extensions to the Probabilistic Multi-Hypothesis Tracker for Improved Data Association, University of Adelaide, Dissertation, 2003.Google Scholar
  25. [25]
    R.L. Streit and T.E. Luginbuhl, “Probabilistic Multi-Hypothesis Tracking,” Naval Undersea Warefare Center Division, NUWC-NPT/10/428 1995.Google Scholar
  26. [26]
    M.A. Tanner, Tools for Statistical Inference. Location: Springer Verlag, Berlin (Germany), 1996.Google Scholar
  27. [27]
    M. Wieneke and W. Koch, “The PMHT: Solutions for Some of its Problems,‘” SPIE – Conferences of Optics and Photonics (Signal and Data Processing of Small Targets), San Diego (USA), 2007.Google Scholar
  28. [28]
    M. Wieneke, W. Koch, “On Sequential Track Extraction using Expectation-Maximization,” EURASIP, special issue “Track Before Detect Algorithms”, 2008.Google Scholar
  29. [29]
    M. Wieneke, K. Safenreiter and W. Koch, “Combined Person Tracking and Classification in a Network of Chemical Sensors,” ISIF – 11th International Conference on Information Fusion, 2008.Google Scholar
  30. [30]
    M. Wieneke, K. Safenreiter and W. Koch, “Hazardous Material Localization and Person Tracking,‘” SPIE – Conferences of Optics and Photonics (Signal and Data Processing of Small Targets), Orlando (USA), 2008.Google Scholar
  31. [31]
    P.K. Willett, Y. Ruan and R.L. Streit, “PMHT: Problems and Some Solutions,” IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 3 , pp. 738–754, 2002.CrossRefGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2009

Authors and Affiliations

  • Christopher Becher
    • 1
  • G.L. Foresti
    • 2
  • Peter Kaul
    • 1
  • W. Koch
    • 3
  • F.P. Lorenz
    • 3
  • D. Lubczyk
    • 4
  • C. Micheloni
    • 2
  • C. Piciarelli
    • 2
  • K. Safenreiter
    • 3
  • C. Siering
    • 4
  • M. Varela
    • 3
  • S.R. Waldvogel
    • 4
  • M. Wieneke
    • 3
  1. 1.Department of Applied Natural SciencesFH Bonn-Rhein-SiegRheinbachGermany
  2. 2.Department of Computer ScienceUniversità degli Studi di UdineUdineItaly
  3. 3.Department of Sensor Data and Information FusionFGAN-FKIEWachtbergGermany
  4. 4.Kekulé-Institute for Organic Chemistry and BiochemistryUniversity of BonnBonnGermany

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