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
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 compar
ed to other algorithms.
(More)