Inferential measurements for situation awareness

Author

Rattadilok, Prapa ; Petrovski, Andrei

Author_Institution

Sch. of Comput. Sci. & Digital Media, Robert Gordon Univ., Aberdeen, UK

fYear

2013

fDate

15-17 July 2013

Firstpage

93

Lastpage

98

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.

Keywords

learning (artificial intelligence); traffic engineering computing; anomalous driving patterns; computational intelligence techniques; inferential measurement systems; machine learning; simulated traffic surveillance data; situation awareness; traffic surveillance; Artificial neural networks; Buildings; Computational intelligence; Computational modeling; Surveillance; Traffic control; Vehicles; anomaly detection; inferential measurement; machine learning; situation awareness; unmanned aerial vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013 IEEE International Conference on

Conference_Location

Milan

Print_ISBN

978-1-4673-4701-3

Type

conf

DOI

10.1109/CIVEMSA.2013.6617402

Filename

6617402