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Authors: Mathias Anneken 1 ; Yvonne Fischer 1 and Jürgen Beyerer 2

Affiliations: 1 Fraunhofer Institute of Optronics and System Technologies and Image Exploitation (Fraunhofer IOSB), Germany ; 2 Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB) and Karlsruhe Institute of Technology (KIT), Germany

ISBN: 978-989-758-172-4

Keyword(s): B-spline Interpolation, Support Vector Machines, Artificial Neural Networks, Multilayer Perceptron, Gaussian Mixture Models, Anomaly Detection, Trajectories, Maritime Domain.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

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 compare d to other algorithms. (More)

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Paper citation in several formats:
Anneken, M.; Fischer, Y. and Beyerer, J. (2016). Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain.In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 250-257. DOI: 10.5220/0005655302500257

@conference{icaart16,
author={Mathias Anneken. and Yvonne Fischer. and Jürgen Beyerer.},
title={Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={250-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005655302500257},
isbn={978-989-758-172-4},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain
SN - 978-989-758-172-4
AU - Anneken, M.
AU - Fischer, Y.
AU - Beyerer, J.
PY - 2016
SP - 250
EP - 257
DO - 10.5220/0005655302500257

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