Paper Info
Title
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.
Year
DOI
Venue
2011
10.1007/978-3-642-19170-1_30
IFIP Advances in Information and Communication Technology
Keywords
Field
DocType
Heterogeneous sensor network,LLF (Low level Feature),HBA (Human Behaviour Analysis),HMM (Hidden Markov Model),LMA (Laban Movement Analysis),Crowd analysis,ATM (Automated Teller Machine) security
Normality,Abnormality,Electronic engineering,Artificial intelligence,Atmosphere (unit),Laban Movement Analysis,Pattern recognition,Audio analyzer,Engineering,Hidden Markov model,Wireless sensor network,Machine learning,Microphone
Conference
Volume
ISSN
Citations 
349
1868-4238
3
PageRank 
References 
Authors
0.46
12
7
Name
Order
Citations
PageRank
Hadi Aliakbarpour19014.79
Kamrad Khoshhal2393.35
João Quintas3348.38
Kamel Mekhnacha41138.52
Julien Ros5364.22
Maria Andersson6223.35
Jorge Dias755651.00