LMA-Based Human Behaviour Analysis Using HMM

In this paper a new body motion-based Human Behaviour Analysing (HBA) approach is proposed for the sake of events classification. Here, the inetersting events are as normal and abnormal behaviours in a 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 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.