Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance

Andrei Petrovski, Prapa Rattadilok, Sergey Petrovskii

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

1 Citation (Scopus)

Abstract

An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in the surveillance data with the help of statistical analysis, computational intelligent and machine learning. Computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data. The experimental results have demonstrated that a reasonable performance is achieved in terms of inferential accuracy and data processing speed.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 17th International Conference, EANN 2016, Proceedings
EditorsLazaros Iliadis, Chrisina Jayne
PublisherSpringer Verlag
Pages161-175
Number of pages15
ISBN (Print)9783319441870
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event17th International Conference on Engineering Applications of Neural Networks, EANN 2016 - Aberdeen, United Kingdom
Duration: 2 Sep 20165 Sep 2016

Publication series

NameCommunications in Computer and Information Science
Volume629
ISSN (Print)1865-0929

Conference

Conference17th International Conference on Engineering Applications of Neural Networks, EANN 2016
Country/TerritoryUnited Kingdom
CityAberdeen
Period2/09/165/09/16

Keywords

  • Artificial neural networks
  • Computational intelligence
  • Cyber physical system
  • Data anomalies
  • Intelligent measurement
  • Traffic surveillance

ASJC Scopus subject areas

  • Computer Science (all)
  • Mathematics (all)

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