Trajectory classification based on machine-learning techniques over tracking data

Jesus García*, Oscar Pérez Concha, José M. Molina, Gonzalo De Miguel

*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

This work addresses the application of a machine-learning approach to classify ATC trajectory segments from recorded opportunity traffic. It is based on the mode probabilities estimated by an IMM tracking filter operating forward and backward over available data. A learning algorithm creates a rule base for classification from these data, once they have been properly prepared. Performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The offline processing of real data allows an accurate classification of manoeuvring segments, with the possibility of synthesizing ground truth lines for performance evaluation.

Original languageEnglish
Title of host publication2006 9th International Conference on Information Fusion, FUSION
Place of PublicationFlorence
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Print)1424409535, 9781424409532, 0972184465
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event2006 9th International Conference on Information Fusion, FUSION - Florence, Italy
Duration: 10 Jul 200613 Jul 2006

Other

Other2006 9th International Conference on Information Fusion, FUSION
CountryItaly
CityFlorence
Period10/07/0613/07/06

Keywords

  • Artificial intelligence
  • Data mining
  • Trajectory classification and reconstruction

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