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HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

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.
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Hadi Aliakbarpour, Kamrad Khoshhal, João Quintas, Kamel Mekhnacha, Julien Ros, et al.. HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network. 2nd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2011, Costa de Caparica, Portugal. pp.277-285, ⟨10.1007/978-3-642-19170-1_30⟩. ⟨hal-01566541⟩



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