Inferential measurements for situation awareness

Prapa Rattadilok, Andrei Petrovski

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 - Proceedings
Pages93-98
Number of pages6
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 - Milan, Italy
Duration: 15 Jul 201317 Jul 2013

Publication series

Name2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013 - Proceedings

Conference

Conference2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2013
Country/TerritoryItaly
CityMilan
Period15/07/1317/07/13

Keywords

  • anomaly detection
  • inferential measurement
  • machine learning
  • situation awareness
  • unmanned aerial vehicles

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

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