TY - GEN
T1 - HMM-based abnormal behaviour detection using heterogeneous sensor network
AU - Aliakbarpour, Hadi
AU - Khoshhal, Kamrad
AU - Quintas, João
AU - Mekhnacha, Kamel
AU - Ros, Julien
AU - Andersson, Maria
AU - Dias, Jorge
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - ATM (Automated Teller Machine) security
KW - Crowd analysis
KW - HBA (Human Behaviour Analysis)
KW - Heterogeneoussensor network
KW - HMM (Hidden Markov Model)
KW - LLF (Low level Feature)
KW - LMA (Laban Movement Analysis)
UR - http://www.scopus.com/inward/record.url?scp=79952223733&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-19170-1_30
DO - 10.1007/978-3-642-19170-1_30
M3 - Conference contribution
AN - SCOPUS:79952223733
SN - 9783642191695
T3 - IFIP Advances in Information and Communication Technology
SP - 277
EP - 285
BT - Technological Innovation for Sustainability - Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, Proceedings
T2 - 2nd IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011
Y2 - 21 February 2011 through 23 February 2011
ER -