LMA-Based Human Behaviour Analysis Using HMM

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

Abstract

In this paper a new body motion-based Human Behaviour Analysing (HBA) approach is proposed for the sake of events classification. Here, the interesting events are as normal and abnormal behaviours in a Automated Teller Machine (ATM) scenario. The concept of Laban Movement Analysis (LMA), which is a known human movement analysing system, is used in order to define and extract sufficient features. A two-phase probabilistic approach have been applied to model the system’s state. Firstly, a Bayesian network is used to estimate LMA-based human movement parameters. Then the sequence of the obtained LMA parameters are used as the inputs of the second phase. As the second phase, the Hidden Markov Model (HMM), which is a well-known approach to deal with the time-sequential data, is used regarding the context of the ATM scenario. The achieved results prove the eligibility and efficiency of the proposed method for the surveillance applications.

Keywords

Human Behaviour Analysing Laban Movement Analysis HMM and Bayesian Network 

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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Kamrad Khoshhal
    • 1
  • Hadi Aliakbarpour
    • 1
  • Kamel Mekhnacha
    • 2
  • Julien Ros
    • 2
  • Joao Quintas
    • 1
  • Jorge Dias
    • 1
  1. 1.Institute of Systems and RoboticsUniversity of CoimbraPortugal
  2. 2.Probayes SASMontbonnotFrance

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