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

Mathias Anneken, Yvonne Fischer, Jürgen Beyerer

2016

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.

References

  1. Anneken, M., Fischer, Y., and Beyerer, J. (2015). Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain. In SAI Intelligent Systems Conference 2015.
  2. Barber, D. (2014). Bayesian Reasoning and Machine Learning. Cambridge University Press.
  3. Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3):15:1-15:58.
  4. Dahlbom, A. and Niklasson, L. (2007). Trajectory clustering for coastal surveillance. In Information Fusion, 2007 10th International Conference on, pages 1-8.
  5. de Vries, G. K. D. and van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications, 39(18):13426 - 13439.
  6. Fischer, Y., Reiswich, A., and Beyerer, J. (2014). Modeling and recognizing situations of interest in surveillance applications. In Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2014 IEEE International Inter-Disciplinary Conference on, pages 209-215.
  7. Gallier, J. (1999). Curves and Surfaces in Geometric Modeling: Theory and Algorithms. Morgan Kaufmann.
  8. Guillarme, N. L. and Lerouvreur, X. (2013). Unsupervised extraction of knowledge from s-ais data for maritime situational awareness. In 16th International Conference on Information Fusion Istanbul, Turkey, July 9- 12, 2013.
  9. Jones, E., Oliphant, T., Peterson, P., et al. (2001). SciPy: Open source scientific tools for Python. [Online; http://www.scipy.org/; accessed 2015-09-01].
  10. Kung, S. Y. (2014). Kernel Methods and Machine Learning. Cambridge University Press.
  11. Laxhammar, R. and Falkman, G. (2011). Sequential conformal anomaly detection in trajectories based on hausdorff distance. In Information Fusion (FUSION), 2011 14th International Conference on, pages 1-8.
  12. Laxhammar, R., Falkman, G., and Sviestins, E. (2009). Anomaly detection in sea traffic - a comparison of the gaussian mixture model and the kernel density. In 12th International Conference on Information Fusion Seattle, WA, USA, July 6-9, 2009.
  13. Melo, J., Naftel, A., Bernardino, A., and Santos-Victor, J. (2006). Detection and classification of highway lanes using vehicle motion trajectories. Intelligent Transportation Systems, IEEE Transactions on, 7(2):188- 200.
  14. Morris, B. and Trivedi, M. (2008). A survey of vision-based trajectory learning and analysis for surveillance. Circuits and Systems for Video Technology, IEEE Transactions on, 18(8):1114-1127.
  15. Naftel, A. and Khalid, S. (2006). Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Systems, 12(3):227-238.
  16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
  17. Rosen, O. and Medvedev, A. (2012). An on-line algorithm for anomaly detection in trajectory data. In American Control Conference (ACC), 2012, pages 1117-1122.
  18. Shalev-Shwartz, S. and Ben-David, S. (2014). Understanding Machine Learning - From Theory to Algorithms. Cambridge University Press.
  19. Shao, H., Japkowicz, N., Abielmona, R., and Falcon, R. (2014). Vessel track correlation and association using fuzzy logic and echo state networks. In Evolutionary Computation (CEC) 2014, IEEE Conference on.
  20. Vakanski, A., Mantegh, I., Irish, A., and Janabi-Sharifi, F. (2012). Trajectory learning for robot programming by demonstration using hidden markov model and dynamic time warping. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 42(4):1039-1052.
  21. Witten, I. H. and Frank, E. (2005). Data Minig: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, 2 edition.
  22. Wojciechowski, M. (2011). Feed-forward neural network for python. [online; http://ffnet.sourceforge.net/; accessed 2015-09-01].
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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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