HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

  • Hadi Aliakbarpour
  • Kamrad Khoshhal
  • João Quintas
  • Kamel Mekhnacha
  • Julien Ros
  • Maria Andersson
  • Jorge Dias
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 349)

Abstract

This paper proposes a HMM-based approach for detecting abnormal situations in some simulated ATM (Automated Teller Machine) scenarios, by using a network of heterogeneous sensors. The applied sensor network comprises of cameras and microphone arrays. The idea is to use such a sensor network in order to detect the normality or abnormality of the scenes in terms of whether a robbery is happening or not. The normal or abnormal event detection is performed in two stages. Firstly, a set of low-level-features (LLFs) is obtained by applying three different classifiers (what are called here as low-level classifiers) in parallel on the input data. The low-level classifiers are namely Laban Movement Analysis (LMA), crowd and audio analysis. Then the obtained LLFs are fed to a concurrent Hidden Markov Model in order to classify the state of the system (what is called here as high-level classification). The attained experimental results validate the applicability and effectiveness of the using heterogeneous sensor network to detect abnormal events in the security applications.

Keywords

Heterogeneoussensor network LLF (Low level Feature) HBA (Human Behaviour Analysis) HMM (Hidden Markov Model) LMA (Laban Movement Analysis) Crowd analysis ATM (Automated Teller Machine) security 

References

  1. 1.
    Eshel, R., Moses, Y.: Homography Based Multiple Camera Detection and Tracking of People in a Dense Crowd. In: IEEE Conference on CVPR 2008 (2008)Google Scholar
  2. 2.
    Bird, N., Atev, S., Caramelli, N., Martin, R., Masoud, O., Papanikolopoulos, N.: Real time, online detection of abandoned objects in public areas. In: Robotics and Automation, ICRA 2006, May15-19, pp. 3775–3780. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar
  3. 3.
    Shang, L., Chan, K.-P.: Nonparametric discriminant HMM and application to facial expression recognition. In: CVPR 2009, June 20-25, pp. 2090–2096. IEEE, Los Alamitos (2009)Google Scholar
  4. 4.
    Drews, P., Quintas, J., Dias, J., Andersson, M., Nygards, J., Rydell, J.: Crowd behavior analysis under cameras network fusion using probabilistic methods. In: The 13th International Conference on Information Fusion, EICC Edinburgh, UK, July 26-29 (2010)Google Scholar
  5. 5.
    Rett, J., Dias, J., Ahuactzin, J.-M.: Laban Movement Analysis using a Bayesian model and perspective projections. Brain, Vision and AI (2008)Google Scholar
  6. 6.
    Khoshhal, K., Aliakbarpour, H., Quintas, J., Drews, P., Dias, J.: Probabilistic LMA-based classification of human behaviour understanding using power spectrum technique. In: 13th Int. Conf. on Information Fusion 2010, EICC Edinburgh, UK, July 10 (2010)Google Scholar
  7. 7.
    Ntalampiras, S., Potamitis, I., Fakotakis, N.: An Adaptive Framework for Acoustic Monitoring of Potential Hazards., EURASIP. Journal on Audio, Speech, and Music Processing Volume (2009) doi:10.1155/2009/594103 Google Scholar
  8. 8.
    Ntalampiras, S., Ganchev, T., Potamitis, I., Fakotakis, N.: Heterogeneous Sensor Database in Support of Human Behaviour Analysis in Unrestricted Environments: The Audio Part The 7th int. conf. on Language Resources and Evaluation, LREC (2010)Google Scholar
  9. 9.
    Zhao, L., Badler, N.I.: Acquiring and validating motion qualities from live limb gestures. Graphical Models, 1–16 (2005)Google Scholar
  10. 10.
    Shi, G., Zou, Y., Jin, Y., Cui, X., Li, W.J.: Towards HMM based human motion recognition using mems inertial sensors. In: Proc. IEEE Int. Conf. Robotics & Biomimetics (2009)Google Scholar
  11. 11.
    Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. pp. 267–296 (1990)Google Scholar
  12. 12.
    Pachter, L., Alexandersson, M., Cawley, S.: Applications of generalized pair Hidden Markov Models to alignment and gene finding problems. In: Proceedings of the 5th Annual Int. Conf. on Computational Biology, RECOMB 2001, New York, USA, pp. 241–248 (2001)Google Scholar
  13. 13.
    Markov, A.: An example of statistical investigation of the text eugene onegin concerning the connection of samples in chains. In: Lecture at the physical-mathematical faculty, Royal Academy of Sciences, St. PetersburgGoogle Scholar
  14. 14.
    Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Biem, A.: A model selection criterion for classification: Application to HMM topology optimization. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, ICDAR 2003, Washington, DC, USA, p. 104. IEEE, Los Alamitos (2003)CrossRefGoogle Scholar
  16. 16.
    Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Aliakbarpour, H., Ferreira, J.F., Khoshhal, K., Dias, J.: A Novel Framework for Data Registration and Data Fusion in Presence of Multi-modal Sensors. In: Camarinha-Matos, L.M., Pereira, P., Ribeiro, L. (eds.) DoCEIS 2010. IFIP Advances in Information and Communication Technology, vol. 314, pp. 308–315. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Aliakbarpour, H., Dias, J.: Human Silhouette Volume Reconstruction Using a Gravity-based Virtual Camera Network. In: The Proceedings of the 13th International Conference on Information Fusion 2010, EICC Edinburgh, UK, July 26-29 (2010)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Hadi Aliakbarpour
    • 1
  • Kamrad Khoshhal
    • 1
  • João Quintas
    • 1
  • Kamel Mekhnacha
    • 2
  • Julien Ros
    • 2
  • Maria Andersson
    • 3
  • Jorge Dias
    • 1
  1. 1.ISRUniversity of CoimbraPortugal
  2. 2.ProbayesFrance
  3. 3.FOILinköpingSweden

Personalised recommendations