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Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning.

Rattadilok, Prapa; Petrovski, Andrei

Authors

Prapa Rattadilok



Abstract

The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measurement system aimed at enhancing situation awareness has been developed and tested on simulated traffic surveillance data. The performance of several Computational Intelligence techniques within this system has been examined and compared on the data containing anomalous driving patterns.

Citation

RATTADILOK, P. and PETROVSKI, A. 2013. Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning. In Proceedings of the 2013 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA 2013), 15-17 July 2013, Milan, Italy. New York: IEEE [online], article number 6617402, pages 93-98. Available from: https://doi.org/10.1109/CIVEMSA.2013.6617402

Conference Name 2013 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA 2013)
Conference Location Milan, Italy
Start Date Jul 15, 2013
End Date Jul 17, 2013
Acceptance Date Jul 31, 2013
Online Publication Date Jul 31, 2013
Publication Date Oct 3, 2013
Deposit Date Feb 12, 2015
Publicly Available Date Feb 12, 2015
Publisher Institute of Electrical and Electronics Engineers
Article Number 6617402
Pages 93-98
Series Title Proceedings of the IEEE international conference on computational intelligence and virtual environments for measurement systems and applications
ISBN 9781467347013
DOI https://doi.org/10.1109/CIVEMSA.2013.6617402
Keywords Inferential measurement; Situation awareness; Machine learning; Anomaly detection; Unmanned aerial vehicles
Public URL http://hdl.handle.net/10059/1145

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