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)


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.


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 


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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

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