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Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain

 
: Anneken, Mathias; Fischer, Yvonne; Beyerer, Jürgen

:
Preprint urn:nbn:de:0011-n-3959826 (8.6 MByte PDF)
MD5 Fingerprint: f450421f84eba15d93f723788a781bb8
Erstellt am: 17.6.2016


Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
8th International Conference on Agents and Artificial Intelligence, ICAART 2016 : Rome, February 2, 2016
Setubal: SciTePress, 2016
ISBN: 978-989-758-172-4
S.250-257
International Conference on Agents and Artificial Intelligence (ICAART) <8, 2016, Rome>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
B-spline Interpolation; Support Vector Machines; Artificial Neural Networks; Multilayer Perceptron; Gaussian Mixture Models; Anomaly Detection; Trajectories; Maritime Domain

Abstract
The detection of anomalies and outliers is an important task for surveillance applications as it supports operators in their decision making process. One major challenge for the operators is to keep focus and not to be overwhelmed by the amount of information supplied by different sensor systems. Therefore, helping an operator to identify important details in the incoming data stream is one possibility to strengthen their situation awareness. In order to achieve this aim, the operator needs a detection system with high accuracy and low false alarm rates, because only then the system can be trusted. Thus, a fast and reliable detection system based on b-spline representation is introduced. Each trajectory is estimated by its cubic b-spline representation. The normal behavior is then learned by different machine learning algorithm like support vector machines and artificial neural networks, and evaluated by using an annotated real dataset from the maritime domain. The results are compared to other algorithms.

: http://publica.fraunhofer.de/dokumente/N-395982.html